Integrated gestural interaction and multi-user collaboration in immersive virtual reality environments

ABSTRACT

The technology disclosed relates to tracking motion of a wearable sensor system using a combination a RGB (red, green, and blue) and IR (infrared) pixels of one or more cameras. In particular, it relates to capturing gross features and feature values of a real world space using RGB pixels and capturing fine features and feature values of the real world space using IR pixels. It also relates to enabling multi-user collaboration and interaction in an immersive virtual environment. It also relates to capturing different sceneries of a shared real world space from the perspective of multiple users. It further relates to sharing content between wearable sensor systems. In further relates to capturing images and video streams from the perspective of a first user of a wearable sensor system and sending an augmented version of the captured images and video stream to a second user of the wearable sensor system.

This application is a continuation of U.S. patent application Ser. No.14/751,056, entitled, “INTEGRATED GESTURAL INTERACTION AND MULTI-USERCOLLABORATION IN IMMERSIVE VIRTUAL REALITY ENVIRONMENTS,” filed on 25Jun. 2015, which claims the benefit of US Provisional Patent ApplicationNo. 62/017,805, entitled, “INTEGRATED GESTURAL INTERACTION ANDMULTI-USER COLLABORATION IN IMMERSIVE VIRTUAL REALITY ENVIRONMENTS,”filed on 26 Jun. 2014. The provisional and non-provisional applicationsare hereby incorporated by reference for all purposes.

FIELD OF THE TECHNOLOGY DISCLOSED

The present disclosure relates generally to human machine interface andin particular to augmented reality for wearable devices and methods ofobject detection and tracking.

INCORPORATIONS

Materials incorporated by reference in this filing include thefollowing:

“DETERMINING POSITIONAL INFORMATION FOR AN OBJECT IN SPACE”, U.S. Non.Prov. application Ser. No. 14/214,605, filed 14 Mar. 2014,

“RESOURCE-RESPONSIVE MOTION CAPTURE”, U.S. Non-Prov. application Ser.No. 14/214,569, filed on 14 Mar. 2014,

“PREDICTIVE INFORMATION FOR FREE-SPACE GESTURE CONTROL ANDCOMMUNICATION”, U.S. Prov. App. No. 61/873,758, filed on 4 Sep. 2013,

“VELOCITY FIELD INTERACTION FOR FREE SPACE GESTURE INTERFACE ANDCONTROL”, U.S. Prov. App. No. 61/891,880, filed on 16 Oct. 2013,

“INTERACTIVE TRAINING RECOGNITION OF FREE SPACE GESTURES FOR INTERFACEAND CONTROL”, U.S. Prov. App. No. 61/872,538, filed on 30 Aug. 2013,

“DRIFT CANCELATION FOR PORTABLE OBJECT DETECTION AND TRACKING”, U.S.Prov. App. No. 61/938,635, filed on 11 Feb. 2014,

“IMPROVED SAFETY FOR WEARABLE VIRTUAL REALITY DEVICES VIA OBJECTDETECTION AND TRACKING”, U.S. Prov. App. No. 61/981,162, filed on 17Apr. 2014,

“WEARABLE AUGMENTED REALITY DEVICES WITH OBJECT DETECTION AND TRACKING”,U.S. Prov. App. No. 62/001,044, filed on 20 May 2014,

“METHODS AND SYSTEMS FOR IDENTIFYING POSITION AND SHAPE OF OBJECTS INTHREE-DIMENSIONAL SPACE”, U.S. Prov. App. No. 61/587,554, filed 17 Jan.2012,

“SYSTEMS AND METHODS FOR CAPTURING MOTION IN THREE-DIMENSIONAL SPACE”,U.S. Prov. App. No. 61/724,091, filed 8 Nov. 2012,

“NON-TACTILE INTERFACE SYSTEMS AND METHODS”, U.S. Prov. App. No.61/816,487, filed 26 Apr. 2013,

“DYNAMIC USER INTERACTIONS FOR DISPLAY CONTROL”, U.S. Prov. App. No.61/752,725, filed on 15 Jan. 2013,

“VEHICLE MOTION SENSORY CONTROL”, U.S. Prov. App. No. 62/005,981, filed30 May 2014,

“SYSTEMS AND METHODS OF PROVIDING HAPTIC-LIKE FEEDBACK INTHREE-DIMENSIONAL (3D) SENSORY SPACE”, U.S. Prov. App. No. 61/937,410,filed 7 Feb. 2014,

“SYSTEMS AND METHODS OF INTERACTING WITH A VIRTUAL GRID IN ATHREE-DIMENSIONAL (3D) SENSORY SPACE”, U.S. Prov. App. No. 61/007,885,filed 4 Jun. 2014,

“SYSTEMS AND METHODS OF GESTURAL INTERACTION IN A PERVASIVE COMPUTINGENVIRONMENT”, U.S. Prov. App. No. 62/003,298, filed 27 May 2014,

“MOTION CAPTURE USING CROSS-SECTIONS OF AN OBJECT”, U.S. applicationSer. No. 13/414,485, filed on 7 Mar. 2012, and

“SYSTEM AND METHODS FOR CAPTURING MOTION IN THREE-DIMENSIONAL SPACE”,U.S. application Ser. No. 13/742,953, filed 16 Jan. 2013.

BACKGROUND

The subject matter discussed in this section should not be assumed to beprior art merely as a result of its mention in this section. Similarly,a problem mentioned in this section or associated with the subjectmatter provided as background should not be assumed to have beenpreviously recognized in the prior art. The subject matter in thissection merely represents different approaches, which in and ofthemselves may also correspond to implementations of the claimedtechnology.

Conventional motion capture approaches rely on markers or sensors wornby the subject while executing activities and/or on the strategicplacement of numerous bulky and/or complex equipment in specialized andrigid environments to capture subject movements. Unfortunately, suchsystems tend to be expensive to construct. In addition, markers orsensors worn by the subject can be cumbersome and interfere with thesubject's natural movement. Further, systems involving large numbers ofcameras tend not to operate in real time, due to the volume of data thatneeds to be analyzed and correlated. Such considerations have limitedthe deployment and use of motion capture technology.

Consequently, there is a need for providing the ability to view and/orinteract with the real world when using virtual reality capable devices(e.g., wearable or otherwise having greater portability) by capturingthe motion of objects in real time without fixed or difficult toconfigure sensors or markers.

INTRODUCTION

The technology disclosed relates to tracking motion of a wearable sensorsystem using a combination a RGB (red, green, and blue) and IR(infrared) pixels of one or more cameras. As a result, the technologydisclosed captures image data along different portions of theelectromagnetic spectrum, including visible, near-IR, and IR bands. Thismultispectral capture compensates for deficiencies in lighting,contrast, and resolution in different environmental conditions.

In one implementation, the technology disclosed relates to capturinggross or coarse features and corresponding feature values of a realworld space using RGB pixels and capturing fine or precise features andcorresponding feature values of the real world space using IR pixels.Once captured, motion information of the wearable sensor system withrespect to at least one feature of the scene is determined based oncomparison between feature values detected at different time instances.For instance, a feature of a real world space is an object at a givenposition in the real world space, and then the feature value can be thethree-dimensional (3D) co-ordinates of the position of the object in thereal world space. If, between pairs of image frame or other imagevolume, the value of the position co-ordinates changes, then this can beused to determine motion information of the wearable sensory system withrespect to the object whose position changed between image frames.

In another example, a feature of a real world space is a wall in thereal world space and the corresponding feature value is orientation ofthe wall as perceived by a viewer engaged with a wearable sensor system.In this example, if a change in the orientation of the wall isregistered between successive image frames captured by a cameraelectronically coupled to the wearable sensor system, then this canindicate a change in the position of the wearable sensor system thatviews the wall.

According to one implementation, RGB pixels of a camera embedded in awearable sensor system are used to identify an object in the real worldspace along with prominent or gross features of the object from an imageor sequence of images such as object contour, shape, volumetric model,skeletal model, silhouettes, overall arrangement and/or structure ofobjects in a real world space. This can be achieved by measuring anaverage pixel intensity of a region or varying textures of regions, asdescribed later in this application. Thus, RGB pixels allow foracquisition of a coarse estimate of the real world space and/or objectsin the real world space.

Further, data from the IR pixels can be used to capture fine or precisefeatures of the real world space, which enhance the data extracted fromRGB pixels. Examples of fine features include surface textures, edges,curvatures, and other faint features of the real world space and objectsin the real world space. In one example, while RGB pixels capture asolid model of a hand, IR pixels are used capture the vein and/or arterypatterns or fingerprints of the hand.

Some other implementations can include capturing image data by using theRGB and IR pixels in different combinations and permutations. Forexample, one implementation can include simultaneously activating theRGB and IR pixels to perform a whole scale acquisition of image data,without distinguishing between coarse or detail features. Anotherimplementation can include using the RGB and IR pixels intermittently.Yet another implementation can include activating the RGB and IR pixelsaccording to a quadratic or Gaussian function. Some otherimplementations can include performing a first scan using the IR pixelsfollowed by an RGB scan, and vice-versa.

The technology disclosed also relates to enabling multi-usercollaboration and interaction in an immersive virtual environment. Inparticular, it relates to capturing different sceneries of a shared realworld space from the perspective of multiple users. In oneimplementation, this is achieved by capturing video streams of the realworld space using cameras embedded in wearable sensor systems engaged bythe multiple users. Also, three-dimensional maps of the real world spaceare determined by extracting one or more feature values of the realworld space from image frames captured using a combination of RGB and IRpixels of the respective cameras. Further, position, orientation, and/orvelocity of the different users and/or their body portions aredetermined by calculating the motion information of their wearablesensor systems with respect to each other. This is achieved by comparingthe respective three-dimensional maps of the real world space generatedfrom the perspective of different users, according to oneimplementation.

The technology disclosed further relates to sharing content betweenwearable sensor systems. In particular, it relates to capturing imagesand video streams from the perspective of a first user of a wearablesensor system and sending an augmented version of the captured imagesand video stream to a second user of the wearable sensor system. Theaugmented version can include corresponding content, with the samecapture frame as the original version, but captured from a wider or moreencompassing field of view than the original version. The augmentedversion can be further used to provide a panoramic experience to thesecond user of the first user's limited view.

In one implementation, the captured content is pre-processed before itis transmitted to a second user. Pre-processing includes enhancing theresolution or contrast of the content or augmenting it with additionalgraphics, annotations, or comments, according to one implementation. Inother implementations, pre-processing includes reducing the resolutionof the captured content before transmission.

In one implementation, a wearable sensor system includes capabilities toautonomously create a three-dimensional (3D) map of an environmentsurrounding a user of a virtual reality device. The map can beadvantageously employed to determine motion information of the wearablesensor system and/or another user in the environment. One methodincludes capturing a plurality of images. A flow can be determined fromfeatures identified in captured images. (For example, features in theimages corresponding to objects in the real world can be detected. Thefeatures of the objects are correlated across multiple images todetermine change, which can be represented as a flow.) Based at least inpart upon that flow, a map of the environment can be created. The methodalso includes localizing a user in the environment using the map.Advantageously, processing time can be reduced when a user enters apreviously visited portion of the environment, since the device needonly scan for new or changed conditions (e.g., that might presenthazards, opportunities or points of interest). In one implementation,once a map of the environment has been built, the map can be presentedto a virtualizing (VR) system and the virtualizing system can use themap as constraint(s) upon which to construct its world. Accordingly, byemploying such techniques, a VR system can enable collaboration betweendifferent users participating in collaborative experiences such asmulti-user games and other shared space activities.

Implementations of the technology disclosed include methods and systemsthat enable a user of a wearable (or portable) virtual reality capabledevice, using a sensor configured to capture motion and/or determiningthe path of an object based on imaging, acoustic or vibrational waves,to view and/or intuitively interact with the real world. Implementationscan enable improved user experience, greater safety, greaterfunctionality to users of virtual reality for machine control and/ormachine communications applications using wearable (or portable)devices, e.g., head mounted devices (HMDs), wearable goggles, watchcomputers, smartphones, and so forth, or mobile devices, e.g.,autonomous and semi-autonomous robots, factory floor material handlingsystems, autonomous mass-transit vehicles, automobiles (human or machinedriven), and so forth, equipped with suitable sensors and processorsemploying optical, audio or vibrational detection.

In one implementation, a wearable sensor system includes capabilities toprovide presentation output to a user of a virtual reality device. Forexample, a video stream including a sequence of images of a scene in thereal world is captured using one or more cameras on a head mounteddevice (HMD) having a set of RGB pixels and a set of IR pixels.Information from the IR sensitive pixels is separated out for processingto recognize gestures. Information from the RGB sensitive pixels isprovided to a presentation interface of the wearable device as a livevideo feed to a presentation output. The presentation output isdisplayed to a user of the wearable sensor system. One or more virtualobjects can be integrated with the video stream images to form thepresentation output. Accordingly, the device is enabled to provide atleast one or all or an combination of the following:

-   -   1. gesture recognition,    -   2. a real world presentation of real world objects via pass        through video feed, and/or    -   3. an augmented reality including virtual objects integrated        with a real world view.

In one implementation, a wearable sensor system includes capabilities toprovide presentation output to a user. For example, in oneimplementation, the device captures a video stream including a sequenceof images of a scene in the real world. The video stream images areintegrated with virtual object(s) to form a presentation output. Thepresentation output is displayed to a user of the wearable sensorsystem. For example, video can be captured with one or more cameras on ahead mounted device (HMD) having a set of RGB pixels and a set of IRpixels.

In one implementation, the ambient lighting conditions are determinedand can be used to adjust display of output. For example, informationfrom the set of RGB pixels is displayed in normal lighting conditionsand information from the set of IR pixels in dark lighting conditions.Alternatively, or additionally, information from the set of IR pixelscan be used to enhance the information from the set of RGB pixels forlow-light conditions, or vice versa. Some implementations can receivefrom a user a selection indicating a preferred display chosen from oneof color imagery from the RGB pixels and IR imagery from the IR pixels,or combinations thereof. Alternatively, or additionally, the deviceitself may dynamically switch between video information captured usingRGB sensitive pixels and video information captured using IR sensitivepixels for display depending upon ambient conditions, user preferences,situational awareness, other factors, or combinations thereof.

In one implementation, information from the IR sensitive pixels isseparated out for processing to recognize gestures; while theinformation from the RGB sensitive pixels is provided to an output as alive video feed; thereby enabling conserving bandwidth to the gesturerecognition processing. In gesture processing, features in the imagescorresponding to objects in the real world can be detected. The featuresof the objects are correlated across multiple images to determinechange, which can be correlated to gesture motions. The gesture motionscan be used to determine command information to a machine under control,application resident thereon or combinations thereof.

In one implementation, motion sensors and/or other types of sensors arecoupled to a motion-capture system to monitor motion of at least thesensor of the motion-capture system resulting from, for example, users'touch. Information from the motion sensors can be used to determinefirst and second positional information of the sensor with respect to afixed point at first and second times. Difference information betweenthe first and second positional information is determined. Movementinformation for the sensor with respect to the fixed point is computedbased upon the difference information. The movement information for thesensor is applied to apparent environment information sensed by thesensor to remove motion of the sensor therefrom to yield actualenvironment information; which can be communicated. Control informationcan be communicated to a system configured to provide a virtual realityor augmented reality experience via a portable device and/or to systemscontrolling machinery or the like based upon motion capture informationfor an object moving in space derived from the sensor and adjusted toremove motion of the sensor itself. In some applications, a virtualdevice experience can be augmented by the addition of haptic, audioand/or visual projectors.

In an implementation, apparent environmental information is capturedfrom positional information of an object portion at the first time andthe second time using a sensor of the motion-capture system. Objectportion movement information relative to the fixed point at the firsttime and the second time is computed based upon the differenceinformation and the movement information for the sensor.

In further implementations, a path of the object is calculated byrepeatedly determining movement information for the sensor, using themotion sensors, and the object portion, using the sensor, at successivetimes and analyzing a sequence of movement information to determine apath of the object portion with respect to the fixed point. Paths can becompared to templates to identify trajectories. Trajectories of bodyparts can be identified as gestures. Gestures can indicate commandinformation to be communicated to a system. Some gestures communicatecommands to change operational modes of a system (e.g., zoom in, zoomout, pan, show more detail, next display page, and so forth).

Advantageously, some implementations can enable improved userexperience, greater to safety and improved functionality for users ofvirtual reality wearable devices. Some implementations further providegesture capability allowing the user to execute intuitive gesturesinvolving virtualized contact with a virtual object. For example, adevice can be provided a capability to distinguish motion of objectsfrom motions of the device itself in order to facilitate proper gesturerecognition. Some implementations can provide improved interfacing witha variety of portable or wearable machines (e.g., smart telephones,portable computing systems, including laptop, tablet computing devices,personal data assistants, special purpose visualization computingmachinery, including heads up displays (HUDs) for use in aircraft orautomobiles for example, wearable virtual and/or augmented realitysystems, including Google Glass, and others, graphics processors,embedded microcontrollers, gaming consoles, or the like; wired orwirelessly coupled networks of one or more of the foregoing, and/orcombinations thereof), obviating or reducing the need for contact-basedinput devices such as a mouse, joystick, touch pad, or touch screen.Some implementations can provide for improved interface with computingand/or other machinery than would be possible with heretofore knowntechniques. In some implementations, a richer human—machine interfaceexperience can be provided.

Other aspects and advantages of the present technology can be seen onreview of the drawings, the detailed description and the claims, whichfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to like partsthroughout the different views. Also, the drawings are not necessarilyto scale, with an emphasis instead generally being placed uponillustrating the principles of the technology disclosed. In thefollowing description, various implementations of the technologydisclosed are described with reference to the following drawings, inwhich:

FIG. 1 illustrates a system for capturing image and other sensory dataaccording to an implementation of the technology disclosed.

FIG. 2 is a simplified block diagram of a computer system implementingimage analysis suitable for supporting a virtual environment enabledapparatus according to an implementation of the technology disclosed.

FIG. 3A is a perspective view from the top of a sensor in accordancewith the technology disclosed, with motion sensors along an edge surfacethereof.

FIG. 3B is a perspective view from the bottom of a sensor in accordancewith the technology disclosed, with motion sensors along the bottomsurface thereof.

FIG. 3C is a perspective view from the top of a sensor in accordancewith the technology disclosed, with detachable motion sensors configuredfor placement on a surface.

FIG. 4 illustrates apparent movement of objects from the perspective ofthe user of a virtual environment enabled apparatus in accordance withthe technology disclosed.

FIG. 5 illustrates apparent movement of objects from the perspective ofthe user of a virtual environment enabled apparatus in accordance withthe technology disclosed.

FIG. 6 shows a flowchart of one implementation of determining motioninformation in a movable sensor apparatus.

FIG. 7 shows a flowchart of one implementation of applying movementinformation to apparent environment information sensed by the sensor toyield actual environment information in a movable sensor apparatus.

FIG. 8 illustrates one implementation of a system for providing avirtual device experience.

FIG. 9 shows a flowchart of one implementation of providing a virtualdevice experience.

FIG. 10 is a flowchart showing a method of tracking motion of a wearablesensor system.

FIG. 11 shows a flowchart of one implementation of creating a multi-userinteractive virtual environment using wearable sensor systems.

FIG. 12 shows a flowchart of sharing content between wearable sensorsystems.

FIGS. 13 and 14 illustrate one implementation of creating a multi-userinteractive virtual environment using wearable sensory systems likeHMDs.

FIGS. 15, 16, 17, and 18 show one implementation of content sharingbetween wearable sensory systems like HMDs in a three-dimensionalsensory real world space.

DETAILED DESCRIPTION

Among other aspects, the technology described herein with reference toexample implementations can provide capabilities to view and/or interactwith the real world to the user of a wearable (or portable) device usinga sensor or sensors configured to capture motion and/or determining thepath of an object based on imaging, acoustic or vibrational waves.Implementations can enable improved user experience, greater safety,greater functionality to users of virtual reality for machine controland/or machine communications applications using wearable (or portable)devices, e.g., head mounted devices (HMDs), wearable goggles, watchcomputers, smartphones, and so forth, or mobile devices, e.g.,autonomous and semi-autonomous robots, factory floor material handlingsystems, autonomous mass-transit vehicles, automobiles (human or machinedriven), and so forth, equipped with suitable sensors and processorsemploying optical, audio or vibrational detection. In someimplementations, projection techniques can supplement the sensory basedtracking with presentation of virtual (or virtualized real) objects(visual, audio, haptic, and so forth) created by applications loadableto, or in cooperative implementation with, the HMD or other device toprovide a user of the device with a personal virtual experience (e.g., afunctional equivalent to a real experience).

Implementations include providing a “pass-through” in which live videois provided to the user of the virtual reality device, either alone orin conjunction with display of one or more virtual objects, enabling theuser to perceive the real world directly. Accordingly, the user isenabled to see an actual desk environment as well as virtualapplications or objects intermingled therewith. Gesture recognition andsensing enables implementations to provide the user with the ability tograsp or interact with real objects (e.g., the user's coke can)alongside the virtual (e.g., a virtual document floating above thesurface of the user's actual desk. In some implementations, informationfrom differing spectral sources is selectively used to drive one oranother aspect of the experience. For example, information from IRsensitive sensors can be used to detect the user's hand motions andrecognize gestures. While information from the visible light region canbe used to drive the pass through video presentation, creating a realworld presentation of real and virtual objects. In a further example,combinations of image information from multiple sources can be used; thesystem—or the user—selecting between IR imagery and visible lightimagery based upon situational, conditional, environmental or otherfactors or combinations thereof. For example, the device can switch fromvisible light imaging to IR imaging when the ambient light conditionswarrant. The user can have the ability to control the imaging source aswell. In yet further examples, information from one type of sensor canbe used to augment, correct, or corroborate information from anothertype of sensor. Information from IR sensors can be used to correct thedisplay of imaging conducted from visible light sensitive sensors, andvice versa. In low-light or other situations not conducive to opticalimaging, where free-form gestures cannot be recognized optically with asufficient degree of reliability, audio signals or vibrational waves canbe detected and used to supply the direction and location of the objectas further described herein.

The technology disclosed can be applied to enhance user experience inimmersive virtual reality environments using wearable sensor systems.Examples of systems, apparatus, and methods according to the disclosedimplementations are described in a “wearable sensor systems” context.The examples of “wearable sensor systems” are being provided solely toadd context and aid in the understanding of the disclosedimplementations. In other instances, examples of gesture-basedinteractions in other contexts like automobiles, robots, or othermachines can be applied to virtual games, virtual applications, virtualprograms, virtual operating systems, etc. Other applications arepossible, such that the following examples should not be taken asdefinitive or limiting either in scope, context, or setting. It willthus be apparent to one skilled in the art that implementations can bepracticed in or outside the “wearable sensor systems” context.

As used herein, a given signal, event or value is “responsive to” apredecessor signal, event or value of the predecessor signal, event orvalue influenced by the given signal, event or value. If there is anintervening processing element, step or time period, the given signal,event or value can still be “responsive to” the predecessor signal,event or value. If the intervening processing element or step combinesmore than one signal, event or value, the signal output of theprocessing element or step is considered “responsive to” each of thesignal, event or value inputs. If the given signal, event or value isthe same as the predecessor signal, event or value, this is merely adegenerate case in which the given signal, event or value is stillconsidered to be “responsive to” the predecessor signal, event or value.“Responsiveness” or “dependency” or “basis” of a given signal, event orvalue upon another signal, event or value is defined similarly.

As used herein, the “identification” of an item of information does notnecessarily require the direct specification of that item ofinformation. Information can be “identified” in a field by simplyreferring to the actual information through one or more layers ofindirection, or by identifying one or more items of differentinformation which are together sufficient to determine the actual itemof information. In addition, the term “specify” is used herein to meanthe same as “identify.”

Refer first to FIG. 1, which illustrates a system 100 for capturingimage data according to one implementation of the technology disclosed.System 100 is preferably coupled to a wearable device 101 that can be apersonal head mounted device (HMD) having a goggle form factor such asshown in FIG. 1, a helmet form factor, or can be incorporated into orcoupled with a watch, smartphone, or other type of portable device.

In various implementations, the system and method for capturing 3Dmotion of an object as described herein can be integrated with otherapplications, such as a HMD or a mobile device. Referring again to FIG.1, a HMD 101 can include an optical assembly that displays a surroundingenvironment or a virtual environment to the user; incorporation of themotion-capture system 100 in the HMD 101 allows the user tointeractively control the displayed environment. For example, a virtualenvironment can include virtual objects that can be manipulated by theuser's hand gestures, which are tracked by the motion-capture system100. In one implementation, the motion-capture system 100 integratedwith the HMD 101 detects a position and shape of user's hand andprojects it on the display of the head mounted device 100 such that theuser can see her gestures and interactively control the objects in thevirtual environment. This can be applied in, for example, gaming orinternet browsing.

System 100 includes any number of cameras 102, 104 coupled to sensoryprocessing system 106. Cameras 102, 104 can be any type of camera,including cameras sensitive across the visible spectrum or with enhancedsensitivity to a confined wavelength band (e.g., the infrared (IR) orultraviolet bands); more generally, the term “camera” herein refers toany device (or combination of devices) capable of capturing an image ofan object and representing that image in the form of digital data. Forexample, line sensors or line cameras rather than conventional devicesthat capture a two-dimensional (2D) image can be employed. The term“light” is used generally to connote any electromagnetic radiation,which may or may not be within the visible spectrum, and may bebroadband (e.g., white light) or narrowband (e.g., a single wavelengthor narrow band of wavelengths).

Cameras 102, 104 are preferably capable of capturing video images (i.e.,successive image frames at a constant rate of at least 15 frames persecond), although no particular frame rate is required. The capabilitiesof cameras 102, 104 are not critical to the technology disclosed, andthe cameras can vary as to frame rate, image resolution (e.g., pixelsper image), color or intensity resolution (e.g., number of bits ofintensity data per pixel), focal length of lenses, depth of field, etc.In general, for a particular application, any cameras capable offocusing on objects within a spatial volume of interest can be used. Forinstance, to capture motion of the hand of an otherwise stationaryperson, the volume of interest might be defined as a cube approximatelyone meter on a side.

As shown, cameras 102, 104 can be oriented toward portions of a regionof interest 112 by motion of the device 101, in order to view avirtually rendered or virtually augmented view of the region of interest112 that can include a variety of virtual objects 116 as well as containan object of interest 114 (in this example, one or more hands) moveswithin the region of interest 112. One or more sensors 108, 110 capturemotions of the device 101. In some implementations, one or more lightsources 115, 117 are arranged to illuminate the region of interest 112.In some implementations, one or more of the cameras 102, 104 aredisposed opposite the motion to be detected, e.g., where the hand 114 isexpected to move. This is an optimal location because the amount ofinformation recorded about the hand is proportional to the number ofpixels it occupies in the camera images, and the hand will occupy morepixels when the camera's angle with respect to the hand's “pointingdirection” is as close to perpendicular as possible. Sensory processingsystem 106, which can be, e.g., a computer system, can control theoperation of cameras 102, 104 to capture images of the region ofinterest 112 and sensors 108, 110 to capture motions of the device 101.Information from sensors 108, 110 can be applied to models of imagestaken by cameras 102, 104 to cancel out the effects of motions of thedevice 101, providing greater accuracy to the virtual experiencerendered by device 101. Based on the captured images and motions of thedevice 101, sensory processing system 106 determines the position and/ormotion of object 114.

For example, as an action in determining the motion of object 114,sensory processing system 106 can determine which pixels of variousimages captured by cameras 102, 104 contain portions of object 114. Insome implementations, any pixel in an image can be classified as an“object” pixel or a “background” pixel depending on whether that pixelcontains a portion of object 114 or not. Object pixels can thus bereadily distinguished from background pixels based on brightness.Further, edges of the object can also be readily detected based ondifferences in brightness between adjacent pixels, allowing the positionof the object within each image to be determined. In someimplementations, the silhouettes of an object are extracted from one ormore images of the object that reveal information about the object asseen from different vantage points. While silhouettes can be obtainedusing a number of different techniques, in some implementations, thesilhouettes are obtained by using cameras to capture images of theobject and analyzing the images to detect object edges. Correlatingobject positions between images from cameras 102, 104 and cancelling outcaptured motions of the device 101 from sensors 108, 110 allows sensoryprocessing system 106 to determine the location in 3D space of object114, and analyzing sequences of images allows sensory processing system106 to reconstruct 3D motion of object 114 using conventional motionalgorithms or other techniques. See, e.g., U.S. patent application Ser.No. 13/414,485, filed on Mar. 7, 2012 and Ser. No. 13/742,953, filed onJan. 16, 2013, and U.S. Provisional Patent Application No. 61/724,091,filed on Nov. 8, 2012, which are hereby incorporated herein by referencein their entirety.

Presentation interface 120 employs projection techniques in conjunctionwith the sensory based tracking in order to present virtual (orvirtualized real) objects (visual, audio, haptic, and so forth) createdby applications loadable to, or in cooperative implementation with, thedevice 101 to provide a user of the device with a personal virtualexperience. Projection can include an image or other visualrepresentation of an object.

One implementation uses motion sensors and/or other types of sensorscoupled to a motion-capture system to monitor motions within a realenvironment. A virtual object integrated into an augmented rendering ofa real environment can be projected to a user of a portable device 101.Motion information of a user body portion can be determined based atleast in part upon sensory information received from imaging devices(e.g. cameras 102, 104) or acoustic or other sensory devices. Controlinformation is communicated to a system based in part on a combinationof the motion of the portable device 101 and the detected motion of theuser determined from the sensory information received from imagingdevices (e.g. cameras 102, 104) or acoustic or other sensory devices.The virtual device experience can be augmented in some implementationsby the addition of haptic, audio and/or other sensory informationprojectors. For example, with reference to FIG. 8, optional videoprojector 120 can project an image of a page (e.g., virtual device 801)from a virtual book object superimposed upon a real world object, e.g.,desk 116 being displayed to a user via live video feed; thereby creatinga virtual device experience of reading an actual book, or an electronicbook on a physical e-reader, even though no book nor e-reader ispresent. Optional haptic projector 806 can project the feeling of thetexture of the “virtual paper” of the book to the reader's finger.Optional audio projector 802 can project the sound of a page turning inresponse to detecting the reader making a swipe to turn the page.Because it is a virtual reality world, the back side of hand 114 isprojected to the user, so that the scene looks to the user as if theuser is looking at the user's own hand(s).

A plurality of sensors 108, 110 coupled to the sensory processing system106 to capture motions of the device 101. Sensors 108, 110 can be anytype of sensor useful for obtaining signals from various parameters ofmotion (acceleration, velocity, angular acceleration, angular velocity,position/locations); more generally, the term “motion detector” hereinrefers to any device (or combination of devices) capable of convertingmechanical motion into an electrical signal. Such devices can include,alone or in various combinations, accelerometers, gyroscopes, andmagnetometers, and are designed to sense motions through changes inorientation, magnetism or gravity. Many types of motion sensors existand implementation alternatives vary widely.

The illustrated system 100 can include any of various other sensors notshown in FIG. 1 for clarity, alone or in various combinations, toenhance the virtual experience provided to the user of device 101. Forexample, in low-light situations where free-form gestures cannot berecognized optically with a sufficient degree of reliability, system 106may switch to a touch mode in which touch gestures are recognized basedon acoustic or vibrational sensors. Alternatively, system 106 may switchto the touch mode, or supplement image capture and processing with touchsensing, when signals from acoustic or vibrational sensors are sensed.In still another operational mode, a tap or touch gesture may act as a“wake up” signal to bring the sensory processing system 106 from astandby mode to an operational mode. For example, the system 106 mayenter the standby mode if optical signals from the cameras 102, 104 areabsent for longer than a threshold interval.

It will be appreciated that the figures shown in FIG. 1 areillustrative. In some implementations, it may be desirable to house thesystem 100 in a differently shaped enclosure or integrated within alarger component or assembly. Furthermore, the number and type of imagesensors, motion detectors, illumination sources, and so forth are shownschematically for the clarity, but neither the size nor the number isthe same in all implementations.

Refer now to FIG. 2, which shows a simplified block diagram of acomputer system 200 for implementing sensory processing system 106.Computer system 200 includes a processor 202, a memory 204, a motiondetector and camera interface 206, a presentation interface 120,speaker(s) 209, a microphone(s) 210, and a wireless interface 211.Memory 204 can be used to store instructions to be executed by processor202 as well as input and/or output data associated with execution of theinstructions. In particular, memory 204 contains instructions,conceptually illustrated as a group of modules described in greaterdetail below, that control the operation of processor 202 and itsinteraction with the other hardware components. An operating systemdirects the execution of low-level, basic system functions such asmemory allocation, file management and operation of mass storagedevices. The operating system may be or include a variety of operatingsystems such as Microsoft WINDOWS operating system, the Unix operatingsystem, the Linux operating system, the Xenix operating system, the IBMAIX operating system, the Hewlett Packard UX operating system, theNovell NETWARE operating system, the Sun Microsystems SOLARIS operatingsystem, the OS/2 operating system, the BeOS operating system, theMACINTOSH operating system, the APACHE operating system, an OPENACTIONoperating system, iOS, Android or other mobile operating systems, oranother operating system of platform.

The computing environment may also include otherremovable/non-removable, volatile/nonvolatile computer storage media.For example, a hard disk drive may read or write to non-removable,nonvolatile magnetic media. A magnetic disk drive may read from orwrites to a removable, nonvolatile magnetic disk, and an optical diskdrive may read from or write to a removable, nonvolatile optical disksuch as a CD-ROM or other optical media. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary operating environment include, but are not limited to,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, and the like. Thestorage media are typically connected to the system bus through aremovable or non-removable memory interface.

Processor 202 may be a general-purpose microprocessor, but depending onimplementation can alternatively be a microcontroller, peripheralintegrated circuit element, a CSIC (customer-specific integratedcircuit), an ASIC (application-specific integrated circuit), a logiccircuit, a digital signal processor, a programmable logic device such asan FPGA (field-programmable gate array), a PLD (programmable logicdevice), a PLA (programmable logic array), an RFID processor, smartchip, or any other device or arrangement of devices that is capable ofimplementing the actions of the processes of the technology disclosed.

Motion detector and camera interface 206 can include hardware and/orsoftware that enables communication between computer system 200 andcameras 102, 104, as well as sensors 108, 110 (see FIG. 1). Thus, forexample, motion detector and camera interface 206 can include one ormore camera data ports 216, 218 and motion detector ports 217, 219 towhich the cameras and motion detectors can be connected (viaconventional plugs and jacks), as well as hardware and/or softwaresignal processors to modify data signals received from the cameras andmotion detectors (e.g., to reduce noise or reformat data) prior toproviding the signals as inputs to a motion-capture (“mocap”) program214 executing on processor 202. In some implementations, motion detectorand camera interface 206 can also transmit signals to the cameras andsensors, e.g., to activate or deactivate them, to control camerasettings (frame rate, image quality, sensitivity, etc.), to controlsensor settings (calibration, sensitivity levels, etc.), or the like.Such signals can be transmitted, e.g., in response to control signalsfrom processor 202, which may in turn be generated in response to userinput or other detected events.

Instructions defining mocap program 214 are stored in memory 204, andthese instructions, when executed, perform motion-capture analysis onimages supplied from cameras and audio signals from sensors connected tomotion detector and camera interface 206. In one implementation, mocapprogram 214 includes various modules, such as an object analysis module222 and a path analysis module 224. Object analysis module 222 cananalyze images (e.g., images captured via interface 206) to detect edgesof an object therein and/or other information about the object'slocation. In some implementations, object analysis module 222 can alsoanalyze audio signals (e.g., audio signals captured via interface 206)to localize the object by, for example, time distance of arrival,multilateration or the like. (“Multilateration is a navigation techniquebased on the measurement of the difference in distance to two or morestations at known locations that broadcast signals at known times. SeeWikipedia, at<http://en.wikipedia.org/w/index.php?title=Multilateration&oldid=523281858>,on Nov. 16, 2012, 06:07 UTC). Path analysis module 224 can track andpredict object movements in 3D based on information obtained via thecameras. Some implementations will include a Virtual Reality/AugmentedReality environment manager 226 provides integration of virtual objectsreflecting real objects (e.g., hand 114) as well as synthesized objects116 for presentation to user of device 101 via presentation interface120 to provide a personal virtual experience. One or more applications230 can be loaded into memory 204 (or otherwise made available toprocessor 202) to augment or customize functioning of device 101 therebyenabling the system 200 to function as a platform. Successive cameraimages are analyzed at the pixel level to extract object movements andvelocities. Audio signals place the object on a known surface, and thestrength and variation of the signals can be used to detect object'spresence. If both audio and image information is simultaneouslyavailable, both types of information can be analyzed and reconciled toproduce a more detailed and/or accurate path analysis. A video feedintegrator 228 provides integration of live video feed from the cameras102, 104 and one or more virtual objects (e.g., 801 of FIG. 8) usingtechniques like that of flowchart 1100 of FIG. 11. Video feed integratorgoverns processing of video information from disparate types of cameras102, 104. For example, information received from pixels sensitive to IRlight and from pixels sensitive to visible light (e.g., RGB) can beseparated by integrator 228 and processed differently. Image informationfrom IR sensors can be used for gesture recognition, while imageinformation from RGB sensors can be provided as a live video feed viapresentation interface 120. Information from one type of sensor can beused to enhance, correct, and/or corroborate information from anothertype of sensor. Information from one type of sensor can be favored insome types of situational or environmental conditions (e.g., low light,fog, bright light, and so forth). The device can select betweenproviding presentation output based upon one or the other types of imageinformation, either automatically or by receiving a selection from theuser. Integrator 228 in conjunction with VR/AR environment 226 controlthe creation of the environment presented to the user via presentationinterface 120.

Presentation interface 120, speakers 209, microphones 210, and wirelessnetwork interface 211 can be used to facilitate user interaction viadevice 101 with computer system 200. These components can be ofgenerally conventional design or modified as desired to provide any typeof user interaction. In some implementations, results of motion captureusing motion detector and camera interface 206 and mocap program 214 canbe interpreted as user input. For example, a user can perform handgestures or motions across a surface that are analyzed using mocapprogram 214, and the results of this analysis can be interpreted as aninstruction to some other program executing on processor 202 (e.g., aweb browser, word processor, or other application). Thus, by way ofillustration, a user might use upward or downward swiping gestures to“scroll” a webpage currently displayed to the user of device 101 viapresentation interface 120, to use rotating gestures to increase ordecrease the volume of audio output from speakers 209, and so on. Pathanalysis module 224 may represent the detected path as a vector andextrapolate to predict the path, e.g., to improve rendering of action ondevice 101 by presentation interface 120 by anticipating movement.

It will be appreciated that computer system 200 is illustrative and thatvariations and modifications are possible. Computer systems can beimplemented in a variety of form factors, including server systems,desktop systems, laptop systems, tablets, smart phones or personaldigital assistants, and so on. A particular implementation may includeother functionality not described herein, e.g., wired and/or wirelessnetwork interfaces, media playing and/or recording capability, etc. Insome implementations, one or more cameras and two or more microphonesmay be built into the computer rather than being supplied as separatecomponents. Further, an image or audio analyzer can be implemented usingonly a subset of computer system components (e.g., as a processorexecuting program code, an ASIC, or a fixed-function digital signalprocessor, with suitable I/O interfaces to receive image data and outputanalysis results).

While computer system 200 is described herein with reference toparticular blocks, it is to be understood that the blocks are definedfor convenience of description and are not intended to imply aparticular physical arrangement of component parts. Further, the blocksneed not correspond to physically distinct components. To the extentthat physically distinct components are used, connections betweencomponents (e.g., for data communication) can be wired and/or wirelessas desired. Thus, for example, execution of object detection module 222by processor 202 can cause processor 202 to operate motion detector andcamera interface 206 to capture images and/or audio signals of an objecttraveling across and in contact with a surface to detect its entrance byanalyzing the image and/or audio data.

FIGS. 3A, 3B, and 3C illustrate three different configurations of amovable sensor system 300A, 300B, and 300C, with reference to exampleimplementations packaged within a single housing as an integratedsensor. In all cases, sensor 300A, 300B, 300C includes a top surface305, a bottom surface 307, and a side wall 310 spanning the top andbottom surfaces 305, 307. With reference also to FIG. 3A, the topsurface 305 of sensor 300A contains a pair of windows 315 for admittinglight to the cameras 102, 104, one of which is optically aligned witheach of the windows 315. If the system includes light sources 115, 117,surface 305 may contain additional windows for passing light to theobject(s) being tracked. In sensor 300A, motion sensors 108, 110 arelocated on the side wall 310. Desirably, the motion sensors are flushwith the surface of side wall 310 so that, the motion sensors aredisposed to sense motions about a longitudinal axis of sensor 300A. Ofcourse, the motion sensors can be recessed from side wall 310 internalto the device in order to accommodate sensor operation and placementwithin available packaging space so long as coupling with the externalhousing of sensor 300A remains adequate. In sensor 300B, motion sensors108, 110 are located proximate to the bottom surface 307, once again ina flush or recessed configuration. The top surface of the sensor 300B(not shown in the figure for clarity sake) contains camera windows 315as shown in FIG. 3A. In FIG. 3C, motion sensors 108, 110 are externalcontact transducers that connect to sensor 300C via jacks 320. Thisconfiguration permits the motion sensors to be located away from thesensor 300C, e.g., if the motion sensors are desirably spaced furtherapart than the packaging of sensor 300C allows. In otherimplementations, movable sensor components of FIGS. 3A, 3B and 3C can beimbedded in portable (e.g., head mounted devices (HMDs), wearablegoggles, watch computers, smartphones, and so forth) or movable (e.g.,autonomous robots, material transports, automobiles (human or machinedriven)) devices.

FIG. 4 illustrates apparent movement of objects from the perspective ofthe user of a virtual environment enabled apparatus 400 in accordancewith the technology. FIG. 4 shows two views of a user of a device 101viewing a field of view 113 at two different times. As shown in block401, at an initial time t₀, user is viewing field of view 113 a usingdevice 101 in a particular initial position to view an area 113 a. Asshown in block 402, device 101 presents to user a display of the devicefield of view 113 a that includes objects 114 (hands) in a particularpose. As shown in block 403, subsequently at time t₁, the user hasrepositioned device 101. Accordingly, the apparent position of objects114 in the field of view 113 b shown in block 404 has changed from theapparent position of the objects 114 in field of view 113 a. Even in thecase where the hands 114 did not move in space, the user sees anapparent movement of the hands 114 due to the change in position of thedevice.

Now with reference to FIG. 5, an apparent movement of one or more movingobjects from the perspective of the user of a virtual environmentenabled apparatus 500 is illustrated. As shown by block 502, field ofview 113 a presented by device 101 at time t₀ includes an object 114. Attime t₀, the position and orientation of tracked object 114 is knownwith respect to device reference frame 120 a, again at time t₀. As shownby block 404, at time t₁, the position and orientation of both devicereference frame 120 b and tracked object 114 have changed. As shown byblock 504, field of view 113 b presented by device 101 at time t₁includes object 114 in a new apparent position. Because the device 101has moved, the device reference frame 120 has moved from an original orstarting device reference frame 120 a to a current or final referenceframe 120 b as indicated by transformation T. It is noteworthy that thedevice 101 can rotate as well as translate. Implementations can providesensing the position and rotation of reference frame 120 b with respectto reference frame 120 a and sensing the position and rotation oftracked object 114 with respect to 120 b, at time t₁. Implementationscan determine the position and rotation of tracked object 114 withrespect to 120 a from the sensed position and rotation of referenceframe 120 b with respect to reference frame 120 a and the sensedposition and rotation of tracked object 114 with respect to 120 b.

In an implementation, a transformation R is determined that moves dashedline reference frame 120 a to dotted line reference frame 120 b, withoutintermediate conversion to an absolute or world frame of reference.Applying the reverse transformation R^(T) makes the dotted linereference frame 120 b lie on top of dashed line reference frame 120 a.Then the tracked object 114 will be in the right place from the point ofview of dashed line reference frame 120 a. (It is noteworthy that R^(T)is equivalent to R⁻¹ for our purposes.) In determining the motion ofobject 114, sensory processing system 106 can determine its location anddirection by computationally analyzing images captured by cameras 102,104 and motion information captured by sensors 108, 110. For example, anapparent position of any point on the object (in 3D space) at time t=

${t_{0}{\text{:}\mspace{14mu}\begin{bmatrix}x \\y \\z \\1\end{bmatrix}}},$can be converted to a real position of the point on the object at time

$t = {t_{1}{\text{:}\mspace{14mu}\begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime} \\1\end{bmatrix}}}$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}\quad$from the frame of reference of the device. We refer to the combinationof a rotation and translation, which are not generally commutative, asthe affine transformation.

The correct location at time t=t₁ of a point on the tracked object withrespect to device reference frame 120 a is given by an inverse affinetransformation, e.g.,

$\begin{bmatrix}R_{ref}^{T} & {{- R_{ref}^{T}}*T_{ref}} \\0 & 1\end{bmatrix}\quad$as provided for in equation (1):

$\begin{matrix}{{\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}*\begin{bmatrix}x \\y \\z \\1\end{bmatrix}} = \begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime} \\1\end{bmatrix}} & (1)\end{matrix}$

Where:

-   -   R_(ref) ^(T)— Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 120 a to the device reference frame 120 b.    -   T_(ref)—Represents translation of the device reference frame 120        a to the device reference frame 120 b.

One conventional approach to obtaining the Affine transform R (from axisunit vector u=(u_(x), u_(y), u_(z)), rotation angle θ) method.Wikipedia, at <http://en.wikipedia.org/wiki/Rotation_matrix>, Rotationmatrix from axis and angle, on Jan. 30, 2014, 20:12 UTC, upon which thecomputations equation (2) are at least in part inspired:

$\begin{matrix}{{R = \begin{bmatrix}{{\cos\mspace{14mu}\theta} + {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{x}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{u_{x}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} \\{{u_{y}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{y}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} \\{{u_{z}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} & {{u_{z}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix}}{R^{T} = {{\begin{bmatrix}{{\cos\mspace{14mu}\theta} + {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{y}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{u_{z}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} \\{{u_{x}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{z}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} \\{{u_{x}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} & {{u_{y}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix} - R^{T}} = \begin{bmatrix}{{{- \cos}\mspace{14mu}\theta} - {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{{- u_{y}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{{- u_{z}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} \\{{{- u_{x}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{{- u_{z}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} \\{{{- u_{x}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} & {{{- u_{y}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} & {{{- \cos}\mspace{14mu}\theta} - {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix}}}{T = \begin{bmatrix}a \\b \\c\end{bmatrix}}} & (2)\end{matrix}$is a vector representing a translation of the object with respect toorigin of the coordinate system of the translated frame,

${{- R^{T}}*T} = \begin{bmatrix}{{( {{{- \cos}\mspace{14mu}\theta} - {u_{x}^{2}( {1 - {\cos\;\theta}} )}} )(a)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} )(b)} + {( {{{- u_{z}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} )(c)}} \\{{( {{{- u_{x}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} )(a)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} )(b)} + {( {{{- u_{z}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} )(c)}} \\{{( {{{- u_{x}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} )(a)} + {( {{{- u_{y}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} )(b)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{z}^{2}( {1 - {\cos\;\theta}} )}} )(c)}}\end{bmatrix}$

In another example, an apparent orientation and position of the objectat time t=t₀: vector pair

$\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix},$can be converted to a real orientation and position of the object attime

$t = {t_{1}{\text{:}\mspace{14mu}\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}}}$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}.$The correct orientation and position of the tracked object with respectto device reference frame at time t=t₀ (120 a) is given by an inverseaffine transformation, e.g.,

$\begin{bmatrix}R_{ref}^{T} & {{- R_{ref}^{T}}*T_{ref}} \\0 & 1\end{bmatrix}\quad$as provided for in equation (3):

$\begin{matrix}{{\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}*\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix}} = \begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}} & (3)\end{matrix}$

Where:

-   -   R_(ref) ^(T)—Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 120 a to the device reference frame 120 b.    -   R_(obj)— Represents a matrix describing the rotation at t₀ of        the object with respect to the device reference frame 120 b.    -   R′_(obj)—Represents a matrix describing the rotation at t₁ of        the object with respect to the device reference frame 120 a.    -   T_(ref)—Represents a vector translation of the device reference        frame 120 a to the device reference frame 120 b.    -   T_(obj)—Represents a vector describing the position at t₀ of the        object with respect to the device reference frame 120 b.    -   T′_(obj)—Represents a vector describing the position at t₁ of        the object with respect to the device reference frame 120 a.

In a yet further example, an apparent orientation and position of theobject at time t=t₀: affine transform

$\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix},$can be converted to a real orientation and position of the object attime

$t = {t_{1}{\text{:}\mspace{14mu}\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}}}$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}.$Furthermore, the position and orientation of the initial reference framewith respect to a (typically) fixed reference point in space can bedetermined using an affine transform

$\begin{bmatrix}R_{init} & T_{init} \\0 & 1\end{bmatrix}.$The correct orientation and position of the tracked object with respectto device reference frame at time t=t₀ (120 a) is given by an inverseaffine transformation, e.g.,

$\begin{bmatrix}R_{init}^{T} & {( {- R_{init}^{T}} )*T_{init}} \\0 & 1\end{bmatrix}\quad$as provided for in equation (4):

$\begin{matrix}{{{\begin{bmatrix}R_{init}^{T} & {( {- R_{init}^{T}} )*T_{init}} \\0 & 1\end{bmatrix}\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}}*\begin{bmatrix}R_{onj} & T_{obj} \\0 & 1\end{bmatrix}} = {\quad\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}}} & (4)\end{matrix}$

Where:

-   -   R^(T) _(init)—Represents a rotation matrix part of an affine        transform describing the rotation transformation at t₀ from the        world reference frame 119 to the device reference frame 120 a.    -   R^(T) _(ref)—Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 120 a to the device reference frame 120 b.    -   R_(obj) Represents a matrix describing the rotation of the        object at t₀ with respect to the device reference frame 120 b.    -   R′_(obj)—Represents a matrix describing the rotation of the        object at t₁ with respect to the device reference frame 120 a.    -   T_(init)—Represents a vector translation at t₀ of the world        reference frame 119 to the device reference frame 120 a.    -   T_(ref)—Represents a vector translation at t₁ of the device        reference frame 120 a to the device reference frame 120 b.    -   T_(obj)—Represents a vector describing the position at t₀ of the        object with respect to the device reference frame 120 b.    -   T′_(obj)—Represents a vector describing the position at t₁ of        the object with respect to the device reference frame 120 a.

In some implementations, the technology disclosed can build a worldmodel with an absolute or world frame of reference. The world model caninclude representations of object portions (e.g. objects, edges ofobjects, prominent vortices) and potentially depth information whenavailable from a depth sensor, depth camera or the like, within theviewpoint of the virtual or augmented reality head mounted sensor. Thesystem can build the world model from image information captured by thecameras of the sensor. Points in 3D space can be determined from thestereo-image information are analyzed to obtain object portions. Thesepoints are not limited to a hand or other control object in aforeground; the points in 3D space can include stationary backgroundpoints, especially edges. The model is populated with the objectportions.

When the sensor moves (e.g., the wearer of a wearable headset turns herhead) successive stereo-image information is analyzed for points in 3Dspace. Correspondences are made between two sets of points in 3D spacechosen from the current view of the scene and the points in the worldmodel to determine a relative motion of the object portions. Therelative motion of the object portions reflects actual motion of thesensor.

Differences in points are used to determine an inverse transformation

$( {{the}\mspace{14mu}\begin{bmatrix}R^{T} & {{- R^{T}}*T} \\0 & 1\end{bmatrix}} )$between model position and new position of object portions. In thisaffine transform, R^(T) describes the rotational portions of motionsbetween camera and object coordinate systems, and T describes thetranslational portions thereof.

The system then applies an inverse transformation of the objectcorresponding to the actual transformation of the device (since thesensor, not the background object moves) to determine the translationand rotation of the camera. Of course, this method is most effectivewhen background objects are not moving relative to the world frame(i.e., in free space).

The model can be updated whenever we detect new points not previouslyseen in the model. The new points are added to the model so that itcontinually grows.

Of course, embodiments can be created in which (1) device cameras areconsidered stationary and the world model is considered to move; or (2)the device cameras are considered to be moving and the world model isconsidered stationary.

The use of a world model described above does not require anygyroscopic, accelerometer or magnetometer sensors, since the samecameras in a single unit (even the same cameras) can sense both thebackground objects and the control object. In any view where the systemcan recognize elements of the model, it can re-localize its position andorientation relative to the model and without drifting from sensor data.In some embodiments, motion sensors can be used to seed the frame toframe transformation and therefore bring correspondences between therendered virtual or augmented reality scenery closer to the sensedcontrol object, making the result less ambiguous (i.e., the system wouldhave an easier time determining what motion of the head had occurred toresult in the change in view from that of the model). In a yet furtherembodiment, sensor data could be used to filter the solution above sothat the motions appear to be smoother from frame to frame, while stillremaining impervious to drift caused by relying upon motion sensorsalone.

Virtual/Augmented Reality

Sensory processing system 106 includes a number of components forgenerating an immersive purely virtual and/or augmented environment. Thefirst component is a camera such as cameras 102 or 104 or other videoinput to generate a digitized video image of the real world oruser-interaction region. The camera can be any digital device that isdimensioned and configured to capture still or motion pictures of thereal world and to convert those images to a digital stream ofinformation that can be manipulated by a computer. For example, cameras102 or 104 can be digital still cameras, digital video cameras, webcams, head-mounted displays, phone cameras, tablet personal computers,ultra-mobile personal computers, and the like.

The second component is a transparent, partially transparent, orsemi-transparent user interface such as display 120 (embedded in a usercomputing device like a wearable goggle or a smartphone) that combinesrendered 3D virtual imagery with a view of the real world, so that bothare visible at the same time to a user. In some implementations, therendered 3D virtual imagery can projected using holographic, laser,stereoscopic, autostereoscopic, or volumetric 3D displays.

In one implementation, a virtual reality and/or augmented reality (AR)environment can be created by instantiation of a free-floating virtualmodality in a real world physical space. In one implementation,computer-generated imagery, presented as free-floating virtual modality,can be rendered in front of a user as reflections using real-timerendering techniques such as orthographic or perspective projection,clipping, screen mapping, rasterizing and transformed into the field ofview or current view space of a live camera embedded in a videoprojector, holographic projection system, smartphone, wearable goggle orother head mounted display (HMD), or heads up display (HUD). In someother implementations, transforming models into the current view spacecan be accomplished using sensor output from onboard sensors. Forexample, gyroscopes, magnetometers and other motion sensors can provideangular displacements, angular rates and magnetic readings with respectto a reference coordinate frame, and that data can be used by areal-time onboard rendering engine to generate 3D imagery. If the userphysically moves a user computing device, resulting in a change of viewof the embedded camera, the virtual modality and computer-generatedimagery can be updated accordingly using the sensor data.

In some implementations, a virtual modality can include a variety ofinformation from a variety of local or network information sources. Someexamples of information include specifications, directions, recipes,data sheets, images, video clips, audio files, schemas, user interfaceelements, thumbnails, text, references or links, telephone numbers, blogor journal entries, notes, part numbers, dictionary definitions, catalogdata, serial numbers, order forms, marketing or advertising and anyother information that may be useful to a user. Some examples ofinformation resources include local databases or cache memory, networkdatabases, Websites, online technical libraries, other devices, or anyother information resource that can be accessed by user computingdevices either locally or remotely through a communication link.

Virtual items in a presentation output, rendered across an interface ofa wearable sensor system, can include text, images, or references toother information (e.g., links). In one implementation, interactivevirtual items can be displayed proximate to their correspondingreal-world objects. In another implementation, interactive virtual itemscan describe or otherwise provide useful information about the objectsto a user.

Projected AR allows users to simultaneously view the real word physicalspace and the interactive virtual items superimposed in the space. Inone implementation, these interactive virtual items can be projected onto the real word physical space using micro-projectors embedded inwearable goggle or other head mounted display (HMD) that cast aperspective view of a stereoscopic 3D imagery onto the real world space.In such an implementation, a camera, in-between the micro-projectors canscan for infrared identification markers placed in the real world space.The camera can use these markers to precisely track the user's headposition and orientation in the real word physical space, according toanother implementation. Yet another implementation includes usingretroreflectors in the real word physical space to prevent scattering oflight emitted by the micro-projectors and to provision multi-userparticipation by maintaining distinct and private user views. In such animplementation, multiple users can simultaneously interact with the samevirtual modality, such that they both view the same virtual objects andmanipulations to virtual objects by one user are seen by the other user.

In other implementations, projected AR obviates the need of usingwearable hardware such as goggles and other hardware like displays tocreate an AR experience. In such implementations, a video projector,volumetric display device, holographic projector, and/or heads-updisplay can be used to create a “glasses-free” AR environment. In oneimplementation, such projectors can be electronically coupled to usercomputing devices such as smartphones or laptop and configured toproduce and magnify virtual items that are perceived as being overlaidon the real word physical space.

The third component is the sensory processing system 106, which capturesa series of sequentially temporal images of a region of interest. Itfurther identifies any gestures performed in the region of interest andcontrols responsiveness of the rendered 3D virtual imagery to theperformed gestures by updating the 3D virtual imagery based on thecorresponding gestures.

Feature Matching

Motion information of a wearable sensor system or a user or body portionof the user can be determined with respect to a feature of a the realworld space that includes the wearable sensory system and/or the user.Some implementations include the features of a real world space beingdifferent real world products or objects in the real world space such asfurniture (chairs, couches, tables, etc.), kitchen appliances (stoves,refrigerators, dishwashers, etc.), office appliances (copy machines, faxmachines, computers), consumer and business electronic devices(telephones, scanners, etc.), furnishings (pictures, wall hangings,sculpture, knick knacks, plants), fixtures (chandeliers and the like),cabinetry, shelving, floor coverings (tile, wood, carpets, rugs), wallcoverings, paint colors, surface textures, countertops (laminate,granite, synthetic countertops), electrical and telecommunication jacks,audio-visual equipment, speakers, hardware (hinges, locks, door pulls,door knobs, etc.), exterior siding, decking, windows, shutters,shingles, banisters, newels, hand rails, stair steps, landscaping plants(trees, shrubs, etc.), and the like, and qualities of all of these (e.g.color, texture, finish, etc.).

As discussed above, a combination of RGB and IR pixels can be used torespectively capture the gross and fine features of the real worldspace. Once captured, changes in features values are detected bycomparing pairs of frames of the captured video stream. In oneimplementation, subpixel refinement of the matches is used to determinethe position of the wearable sensory system with respect to the analyzedfeature. In another implementation, a feature in one image is matched toevery feature within a fixed distance from it in the successive imagesuch that all features that are within a certain disparity limit fromeach other. In other implementations, normalized correlation over aspecified window can be used to evaluate the potential matches.

Some other implementations include copying each identified feature froma frame and storing the feature as a vector. Further, a scalar productof the identified feature vectors is calculated and a mutual consistencycheck is applied such that a feature with highest normalized correlationis considered to be determinative and changes in the feature values(position, orientation) of the feature are used to calculate motioninformation of the wearable sensory system. In other implementations,sum of absolute differences (SAD) can be used to identify thedeterminative feature in a real world space.

FIG. 6 shows a flowchart 600 of one implementation of determining motioninformation in a movable sensor apparatus. Flowchart 600 can beimplemented at least partially with a computer or other data processingsystem, e.g., by one or more processors configured to receive orretrieve information, process the information, store results, andtransmit the results. Other implementations may perform the actions indifferent orders and/or with different, fewer or additional actions thanthose illustrated in FIG. 6. Multiple actions can be combined in someimplementations. For convenience, this flowchart is described withreference to the system that carries out a method. The system is notnecessarily part of the method.

At action 610, a first positional information of a portable or movablesensor is determined with respect to a fixed point at a first time. Inone implementation, first positional information with respect to a fixedpoint at a first time t=t₀ is determined from one or motion sensorsintegrated with, or coupled to, a device including the portable ormovable sensor. For example, an accelerometer can be affixed to device101 of FIG. 1 or sensor 300 of FIG. 3, to provide accelerationinformation over time for the portable or movable device or sensor.Acceleration as a function of time can be integrated with respect totime (e.g., by sensory processing system 106) to provide velocityinformation over time, which can be integrated again to providepositional information with respect to time. In another example,gyroscopes, magnetometers or the like can provide information at varioustimes from which positional information can be derived. These items arewell known in the art and their function can be readily implemented bythose possessing ordinary skill. In another implementation, a secondmotion-capture sensor (e.g., such as sensor 300 of FIG. 3 for example)is disposed to capture position information of the first sensor (e.g.,affixed to 101 of FIG. 1 or sensor 300 of FIG. 3) to provide positionalinformation for the first sensor.

At action 620, a second positional information of the sensor isdetermined with respect to the fixed point at a second time t=t₁.

At action 630, difference information between the first positionalinformation and the second positional information is determined.

At action 640, movement information for the sensor with respect to thefixed point is computed based upon the difference information. Movementinformation for the sensor with respect to the fixed point is can bedetermined using techniques such as discussed above with reference toequations (2).

At action 650, movement information for the sensor is applied toapparent environment information sensed by the sensor to remove motionof the sensor therefrom to yield actual environment information. Motionof the sensor can be removed using techniques such as discussed abovewith reference to FIGS. 4-5.

At action 660, actual environment information is communicated.

FIG. 7 shows a flowchart 700 of one implementation of applying movementinformation for the sensor to apparent environment information (e.g.,apparent motions of objects in the region of interest 112 as sensed bythe sensor) to remove motion of the sensor therefrom to yield actualenvironment information (e.g., actual motions of objects in the regionof interest 112 relative to the reference frame 120 a). Flowchart 700can be implemented at least partially with a computer or other dataprocessing system, e.g., by one or more processors configured to receiveor retrieve information, process the information, store results, andtransmit the results. Other implementations may perform the actions indifferent orders and/or with different, fewer or additional actions thanthose illustrated in FIG. 7. Multiple actions can be combined in someimplementations. For convenience, this flowchart is described withreference to the system that carries out a method. The system is notnecessarily part of the method.

At action 710, positional information of an object portion at the firsttime and the second time are captured.

At action 720, object portion movement information relative to the fixedpoint at the first time and the second time is computed based upon thedifference information and the movement information for the sensor.

At action 730, object portion movement information is communicated to asystem.

Some implementations will be applied to virtual reality or augmentedreality applications. For example, and with reference to FIG. 8, whichillustrates a system 800 for projecting a virtual device augmentedreality experience 801 including one or more real objects, e.g., a desksurface medium 116 according to one implementation of the technologydisclosed. System 800 includes a sensory processing system 106controlling a variety of sensors and projectors, such as for example oneor more cameras 102, 104 (or other image sensors) and optionally someillumination sources 115, 117 comprising an imaging system. Optionally,a plurality of vibrational (or acoustical) sensors 808, 810 positionedfor sensing contacts with desk 116 can be included. Optionallyprojectors under control of system 106 can augment the virtual deviceexperience 801, such as an optional audio projector 802 to provide forexample audio feedback, optional video projector 804, an optional hapticprojector 806 to provide for example haptic feedback to a user ofvirtual device experience 801. For further information on projectors,reference may be had to “Visio-Tactile Projector” YouTube<https://www.youtube.com/watch?v=Bb0hNMxxewg> (accessed Jan. 15, 2014).In operation, sensors and projectors are oriented toward a region ofinterest 112, that can include at least a portion of a desk 116, or freespace 112 in which an object of interest 114 (in this example, a hand)moves along the indicated path 118. One or more applications 821 and 822can be provided as virtual objects integrated into the display of theaugmented reality 113. Accordingly, user (e.g., owner of hand 114) isable to interact with real objects e.g., desk 816, cola 817, in the sameenvironment as virtual objects 801.

FIG. 9 shows a flowchart 900 of one implementation of providing avirtual device experience. Flowchart 900 can be implemented at leastpartially with a computer or other data processing system, e.g., by oneor more processors configured to receive or retrieve information,process the information, store results, and transmit the results. Otherimplementations may perform the actions in different orders and/or withdifferent, fewer or additional actions than those illustrated in FIG. 9.Multiple actions can be combined in some implementations. Forconvenience, this flowchart is described with reference to the systemthat carries out a method. The system is not necessarily part of themethod.

At action 910, a virtual device is projected to a user. Projection caninclude an image or other visual representation of an object. Forexample, visual projection mechanism 120 of FIG. 8 can project a page(e.g., virtual device 801) from a book into a virtual environment 801(e.g., surface portion 116 or in space 112) of a reader; therebycreating a virtual device experience of reading an actual book, or anelectronic book on a physical e-reader, even though no book nor e-readeris present. In some implementations, optional haptic projector 806 canproject the feeling of the texture of the “virtual paper” of the book tothe reader's finger. In some implementations, optional audio projector802 can project the sound of a page turning in response to detecting thereader making a swipe to turn the page.

At action 920, using an accelerometer, moving reference frameinformation of a head mounted device (or hand-held mobile device)relative to a fixed point on a human body is determined.

At action 930, body portion movement information is captured. Motion ofthe body portion can be detected via sensors 108, 110 using techniquessuch as discussed above with reference to FIG. 6.

At action 940, control information is extracted based partly on the bodyportion movement information with respect to the moving reference frameinformation. For example, repeatedly determining movement informationfor the sensor and the object portion at successive times and analyzinga sequence of movement information can be used to determine a path ofthe object portion with respect to the fixed point. For example, a 3Dmodel of the object portion can be constructed from image sensor outputand used to track movement of the object over a region of space. Thepath can be compared to a plurality of path templates and identifying atemplate that best matches the path. The template that best matches thepath control information to a system can be used to provide the controlinformation to the system. For example, paths recognized from an imagesequence (or audio signal, or both) can indicate a trajectory of theobject portion such as a gesture of a body portion.

At action 950, control information can be communicated to a system. Forexample, a control information such as a command to turn the page of avirtual book can be sent based upon detecting a swipe along the desksurface of the reader's finger. Many other physical or electronicobjects, impressions, feelings, sensations and so forth can be projectedonto surface 116 (or in proximity thereto) to augment the virtual deviceexperience and applications are limited only by the imagination of theuser.

FIG. 10 is a flowchart showing a method 1000 of tracking motion of awearable sensor system. Flowchart 1000 can be implemented at leastpartially with a computer or other data processing system, e.g., by oneor more processors configured to receive or retrieve information,process the information, store results, and transmit the results. Otherimplementations may perform the actions in different orders and/or withdifferent, fewer or additional actions than those illustrated in FIG.10. Multiple actions can be combined in some implementations. Forconvenience, this flowchart is described with reference to the systemthat carries out a method. The system is not necessarily part of themethod.

At action 1010, a video stream of a scene of a real world space iscaptured using at least one camera electronically coupled to a wearablesensor system.

At action 1020, one or more feature values of the scene are detectedfrom a plurality of images of the video stream captured at times t0 andt1 using a set of RGB pixels and a set of IR pixels of the camera. Inone implementation, the wearable sensor system has moved between t0 andt1.

At action 1030, motion information of the wearable sensor system isdetermined with respect to at least one feature of the scene based oncomparison between feature values detected at times t0 and t1.

At action 1040, a presentation output is generated for display across aninterface of the wearable sensor display based on information from thesets of RGB and IR pixels.

At action 1050, responsiveness of the presentation output isautomatically calibrated based on the determined motion information ofthe wearable sensor system with respect to the at least one feature ofthe scene. In one implementation, perceived field of view of thepresentation output is proportionally adjusting responsive to thedetermined motion information of the wearable sensor system with respectto the at least one feature of the scene.

In yet another implementation, motion information of a body portionengaged with the wearable sensory system is determined based on themotion information of the wearable sensor system.

In some implementations, gross features of the real world space areextracted using RGB pixels that respectively capture red, green, andblue components of illumination in the scene.

In other implementations, fine features of the real world space areextracted using IR pixels that capture infrared components ofillumination in the scene. In one implementation, fine features of thereal world space include surface texture of the real world space. Inanother implementation, fine features of the real world space includeedges of the real world space. In some another implementation, finefeatures of the real world space include curvatures of the real worldspace. In yet another implementation, fine features of the real worldspace include surface texture of objects in the real world space. In afurther implementation, fine features of the real world space includeedges of objects in the real world space.

In some implementations, fine features of the real world space includecurvatures of objects in the real world space. In anotherimplementation, a feature of the scene is an object in the real worldspace. In some other implementation, a feature value of the scene isorientation of the object. In yet another implementation, a featurevalue of the scene is position of the object. In a furtherimplementation, a feature of the scene is an arrangement of plurality ofobjects in the real world space. In other implementations, a featurevalue of the scene is position of the objects with respect to each otherin the arrangement.

According to some implementations, comparison between feature valuesincludes detecting a change in rotation between the images captured attimes t0 and t1. According to other implementations, comparison betweenfeature values includes detecting a change in translation between theimages captured at times t0 and t1.

In yet other implementations, motion information of the wearable sensorsystem is determined with respect to at least one feature of the sceneby matching features in images captured at time t0 with correspondingfeatures in images captured at time t1. In one implementation, thematched features are within a threshold distance.

In another implementation, motion information of the wearable sensorsystem is determined with respect to at least one feature of the sceneby calculating displacement between the images captured at times t0 andt1 based on at least one of RGB and IR pixel values.

In one implementation, the motion information includes position of thewearable sensor system. In another implementation, the motioninformation includes orientation of the wearable sensor system. In yetanother implementation, the motion information includes velocity of thewearable sensor system. In a further implementation, the motioninformation includes acceleration of the wearable sensor system.

Some implementations include using monocular vision to capture the videostream. Other implementations include using stereoscopic vision tocapture the video stream. Yet other implementations including more thantwo cameras to capture the video stream.

In one implementation, the images captured at times t0 and t1 aresuccessive image pairs. In another implementation, the images capturedat times t0 and t1 are alternative image pairs. In a furtherimplementation, the images captured at times t0 and t1 are alternativeimage pairs. In yet another implementation, the images captured areright and left stereo images captured simultaneously.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. Other implementationscan include a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation can include a systemincluding memory and one or more processors operable to executeinstructions, stored in the memory, to perform any of the methodsdescribed above.

FIG. 11 shows a flowchart 1100 of one implementation of creating amulti-user interactive virtual environment using wearable sensorsystems. Flowchart 1100 can be implemented at least partially with acomputer or other data processing system, e.g., by one or moreprocessors configured to receive or retrieve information, process theinformation, store results, and transmit the results. Otherimplementations may perform the actions in different orders and/or withdifferent, fewer or additional actions than those illustrated in FIG.11. Multiple actions can be combined in some implementations. Forconvenience, this flowchart is described with reference to the systemthat carries out a method. The system is not necessarily part of themethod.

At action 1110, a first video stream of a real world space is capturedusing at least one camera electronically coupled to a first wearablesensor system engaged by a first user.

At action 1120, a second video stream of a real world space is capturedusing at least one camera electronically coupled to a second wearablesensor system engaged by a second user.

At action 1130, respective three-dimensional maps of the real worldspace are generated using sets of RGB and IR pixels of the first andsecond cameras by extracting one or more feature values of the realworld space from the first and second video streams. In oneimplementation, generating respective three-dimensional maps furtherincludes determining a graph of features of the real world space basedon the extracted feature values.

At action 1140, motion information of the first and second wearablesensor systems is determined with respect to each other based oncomparison between the respective three-dimensional maps of the realworld space.

At action 1150, responsiveness of the presentation outputs isautomatically calibrated based on the determined motion information ofthe first and second wearable sensor systems with respect to each other.In some implementations, presentation outputs are generated for displayacross respective interfaces of the first and second wearable sensorsystems based on information from the sets of RGB and IR pixels of thefirst and second cameras. In other implementations, respective perceivedfields of view of the presentation outputs are proportionally adjustedresponsive to the determined motion information of the first and secondwearable sensor systems with respect to each other.

Some other implementations include determining motion information ofrespective body portions of the first and second users based on themotion information of the first and second wearable sensor systems withrespect to each other.

In some implementations, gross features of the real world space areextracted using RGB pixels that respectively capture red, green, andblue components of illumination in the scene.

In other implementations, fine features of the real world space areextracted using IR pixels that capture infrared components ofillumination in the scene. In one implementation, fine features of thereal world space include surface texture of the real world space. Inanother implementation, fine features of the real world space includeedges of the real world space. In some another implementation, finefeatures of the real world space include curvatures of the real worldspace. In yet another implementation, fine features of the real worldspace include surface texture of objects in the real world space. In afurther implementation, fine features of the real world space includeedges of objects in the real world space.

In some implementations, fine features of the real world space includecurvatures of objects in the real world space. In anotherimplementation, a feature of the scene is an object in the real worldspace. In some other implementation, a feature value of the scene isorientation of the object. In yet another implementation, a featurevalue of the scene is position of the object. In a furtherimplementation, a feature of the scene is an arrangement of plurality ofobjects in the real world space. In other implementations, a featurevalue of the scene is position of the objects with respect to each otherin the arrangement.

According to some implementations, comparison between feature valuesincludes detecting a change in rotation between the images captured attimes t0 and t1. According to other implementations, comparison betweenfeature values includes detecting a change in translation between theimages captured at times t0 and t1.

In yet other implementations, motion information of the wearable sensorsystem is determined with respect to at least one feature of the sceneby matching features in images captured at time t0 with correspondingfeatures in images captured at time t1. In one implementation, thematched features are within a threshold distance.

In another implementation, motion information of the wearable sensorsystem is determined with respect to at least one feature of the sceneby calculating displacement between the images captured at times t0 andt1 based on at least one of RGB and IR pixel values.

In one implementation, the motion information includes position of thewearable sensor system. In another implementation, the motioninformation includes orientation of the wearable sensor system. In yetanother implementation, the motion information includes velocity of thewearable sensor system. In a further implementation, the motioninformation includes acceleration of the wearable sensor system.

Some implementations include using monocular vision to capture the videostream. Other implementations include using stereoscopic vision tocapture the video stream. Yet other implementations including more thantwo cameras to capture the video stream.

In one implementation, the images captured at times t0 and t1 aresuccessive image pairs. In another implementation, the images capturedat times t0 and t1 are alternative image pairs. In a furtherimplementation, the images captured at times t0 and t1 are alternativeimage pairs. In yet another implementation, the images captured areright and left stereo images captured simultaneously.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. Other implementationscan include a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation can include a systemincluding memory and one or more processors operable to executeinstructions, stored in the memory, to perform any of the methodsdescribed above.

FIG. 12 shows a flowchart 1200 of sharing content between wearablesensor systems. Flowchart 1200 can be implemented at least partiallywith a computer or other data processing system, e.g., by one or moreprocessors configured to receive or retrieve information, process theinformation, store results, and transmit the results. Otherimplementations may perform the actions in different orders and/or withdifferent, fewer or additional actions than those illustrated in FIG.12. Multiple actions can be combined in some implementations. Forconvenience, this flowchart is described with reference to the systemthat carries out a method. The system is not necessarily part of themethod.

At action 1210, a first video stream of a real world space is capturedat time t0 using at least one camera electronically coupled to a firstwearable sensor system engaged by a first user. In one implementation,the first video stream is captured at a field of view of the first user.

At action 1220, a second video stream of the real world space iscaptured at the time t0 using at least one camera electronically coupledto the first wearable sensor system. In one implementation, the secondvideo stream is captured at a field of view of the camera.

At action 1230, a communication channel is established between the firstwearable sensor system and a second wearable sensor system and thesecond video stream is transmitted to the second wearable sensor system.

This method and other implementations of the technology disclosed caninclude one or more of the following features and/or features describedin connection with additional methods disclosed. In the interest ofconciseness, the combinations of features disclosed in this applicationare not individually enumerated and are not repeated with each base setof features.

In some implementations, the second video stream is preprocessed toenhance resolution and sending the preprocessed second video stream viathe communication channel to the second wearable sensor system.

In other implementations, the second video stream is preprocessed toreduce bandwidth and sending the preprocessed second video stream viathe communication channel to the second wearable sensor system.

In one implementation, the field of view of the at least one camerasubstantially overlaps with the field of view of the user. In anotherimplementation, the field of view of the at least one camera encompassesand exceeds the field of view of the user. In yet anotherimplementation, the field of view of the at least one camera narrows anddeceeds the field of view of the user. In some other implementation, thefield of view of the at least one camera is separate and additional tothe field of view of the user.

In one implementation, short-beam illumination elements are used tocapture a narrow-field of view. In some implementations, the short-beamillumination elements have a beam angle of approximately 60°. In anotherimplementation, wide-beam illumination elements are used to capture abroad-field of view. In some implementations, the wide-beam illuminationelements have a beam angle of approximately 120°.

In some implementations, the second video stream is transmitted to thesecond sensor system in response to user selection.

Typically, a “wide beam” is about 120° wide and a narrow beam isapproximately 60° wide, although these are representative figures onlyand can vary with the application; more generally, a wide beam can havea beam angle anywhere from >90° to 180°, and a narrow beam can have abeam angle anywhere from >0° to 90°. For example, the detection spacecan initially be lit with one or more wide-beam lighting elements with acollective field of view similar to that of the tracking device, e.g., acamera. Once the object's position is obtained, the wide-beam lightingelement(s) can be turned off and one or more narrow-beam lightingelements, pointing in the direction of the object, activated. As theobject moves, different ones of the narrow-beam lighting elements areactivated. In many implementations, these directional lighting elementsonly need to be located in the center of the field of view of thecamera; for example, in the case of hand tracking, people will not oftentry to interact with the camera from a wide angle and a large distancesimultaneously.

If the tracked object is at a large angle to the camera (i.e., far tothe side of the motion-tracking device), it is likely relatively closeto the device. Accordingly, a low-power, wide-beam lighting element canbe suitable in some implementations. As a result, the lighting array caninclude only one or a small number of wide-beam lighting elements closeto the camera along with an equal or larger number of narrow-beamdevices (e.g., collectively covering the center-field region of space infront of the camera—for example, within a 30° or 45° cone around thenormal to the camera). Thus, it is possible to decrease or minimize thenumber of lighting elements required to illuminate a space in whichmotion is detected by using a small number of wide-beam elements and alarger (or equal) number of narrow-beam elements directed toward thecenter field.

It is also possible to cover a wide field of view with many narrow-beamLEDs pointing in different directions, according to otherimplementations. These can be operated so as to scan the monitored spacein order to identify the elements actually spotlighting the object; onlythese are kept on and the others turned off In some embodiments, themotion system computes a predicted trajectory of the tracked object, andthis trajectory is used to anticipate which illumination elements shouldbe activated as the object moves. The trajectory is revised, along withthe illumination pattern, as new tracking information is obtained.

Other implementations can include a non-transitory computer readablestorage medium storing instructions executable by a processor to performany of the methods described above. Yet another implementation caninclude a system including memory and one or more processors operable toexecute instructions, stored in the memory, to perform any of themethods described above.

FIGS. 13-14 illustrate one implementation of creating a multi-userinteractive virtual environment 1300 using wearable sensory systems likeHMDs 1302 and 1304. In particular, block 1301 shows that a first videostream of a real world space (e.g. a kitchen with users 1 and 2) iscaptured using a first camera of HMD 1302 worn by user 1. Similarly,block 1303 depicts that a second video stream of the same real worldspace is captured using a second camera of the HMD 1304 worn by user 2.The respective fields of view 1309 and 1311 of the two HMDs 1302 and1304 are opposite to each other i.e. 180° apart because users 1 and 2are facing each other. As a result, map 1313 for user 1 includes user 2and vice-versa. In other implementations, fields of view 1309 and 1311can be any angle(s) apart from each other, such that they can overlapeach other or be divergent from each other to certain degree(s) orentirely.

Further, respective three-dimensional maps 1313 (block 1305) and 1315(block 1307) are generated using RGB and IR pixels of the first andsecond cameras employing stereoscopic vision technology. In oneimplementation, the three-dimensional maps 1313 and 1315 are generatedusing particular features of the real world space (e.g. a kettle in thekitchen). In other implementations, the three-dimensional maps 1313 and1315 are generated using time of flight (TOF) information from the firstand second cameras serving as TOF cameras. In yet other implementations,the three-dimensional maps 1313 and 1315 are generated using beam-formedsignals, as described in “DETERMINING POSITIONAL INFORMATION FOR ANOBJECT IN SPACE”, U.S. Non-Prov. application Ser. No. 14/523,828, filedon 24 Oct. 2014, which is incorporated by reference in this application.In further implementations, the three-dimensional maps 1313 and 1315 aregenerated using reflected light, as described in “DETERMINING POSITIONALINFORMATION FOR AN OBJECT IN SPACE”, U.S. Non-Prov. application Ser. No.14/214,605, filed on 14 Mar. 2014, which is incorporated by reference inthis application.

Advancing further, the three-dimensional maps 1313 and 1315 are comparedby comparing prominent features (e.g. lamps or chimney in the kitchen)of the real world space in the respective maps 1313 and 1315 andidentifying differences in the prominent features. Further, differencesin the prominent features are used to determine positional differencesbetween HMDs 1302 and 1304 using triangulation techniques disclosed inU.S. Non-Prov. application Ser. No. 14/523,828.

In another implementation, position information of HMDs 1302 and 1304 isdetermined by detecting the HMDs in each other's fields of view 1309 and1311 (e.g. detecting LEDs of the HMDs) and then applying thetriangulation techniques disclosed in U.S. Non-Prov. application Ser.No. 14/523,828 to determine relative positions of the HMDs with respectto each other. The relative positions of the HMDs with respect to eachother provide the corresponding fields of view 1309 and 1311. Further,image information of the three-dimensional maps 1313 and 1315 isdetermined from the fields of view 1309 and 1311. Then, imageinformation for the first map 1313 is aligned with the image informationof the second map 1315 to identify similar prominent features in themaps. In other implementations, image information from the two maps iscombined to create a larger, more comprehensive and inclusivethree-dimensional map of the real world space.

FIG. 14 depicts one implementation of comparing 1400 three-dimensionalmaps 1313 and 1315 to determine motion information of HMDs 1302 and1304. The position and orientation of three-dimensional map 1313 isknown with respect to device reference frame 1317 of HMD 1302. Theposition and orientation of HMD 1302's device reference frame 1319 andthree-dimensional map 1315 are respectively different from that ofdevice reference frame 1317 and three-dimensional map 1313. Thedifference in position and orientation of device reference frames 1317and 1319 is indicated by transformation T. Implementations can providesensing the position and rotation of reference frame 1319 with respectto reference frame 1317 and sensing the position and rotation of trackedcommon features (e.g. kettle, chimney, lamps, or counter in the kitchen)in maps 1313 and 1315 with respect to 1319. Implementations candetermine the position and rotation of tracked common features withrespect to 1317 from the sensed position and rotation of reference frame1319 with respect to reference frame 1317 and the sensed position androtation of tracked common features with respect to 1319.

In an implementation shown in block 1402, a transformation R isdetermined that moves dashed line reference frame 1317 to dotted linereference frame 1319, without intermediate conversion to an absolute orworld frame of reference. Applying the reverse transformation R^(T)makes the dotted line reference frame 1319 lie on top of dashed linereference frame 1317. Then the three-dimensional maps 1313 and 1315 canoverlap from the point of view of dashed line reference frame 1317. (Itis noteworthy that R^(T) is equivalent to R⁻¹ for our purposes.) Indetermining the motion of tracked common features, sensory processingsystem 106 can determine their location and direction by computationallyanalyzing the three-dimensional maps 1313 and 1315 and using motioninformation captured by sensors 108, 110. For example, position of anypoint on the three-dimensional map 1313 at time

${t = {t_{0}{\text{:}\mspace{14mu}\begin{bmatrix}x \\y \\z \\1\end{bmatrix}}}},$can be converted to a position of the point on the three-dimensional map1315 at time

$t = {t_{1}{\text{:}\mspace{14mu}\begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime} \\1\end{bmatrix}}}$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}\quad$from the frame of reference of the device. We refer to the combinationof a rotation and translation, which are not generally commutative, asthe affine transformation.

The correct location at time t=t₁ of a point on the tracked commonfeatures with respect to device reference frame 1317 is given by aninverse affine transformation, e.g.,

$\begin{bmatrix}R_{ref}^{T} & {{- R_{ref}^{T}}*T_{ref}} \\0 & 1\end{bmatrix}\quad$as provided for in equation (1):

$\begin{matrix}{{\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}*\begin{bmatrix}x \\y \\z \\1\end{bmatrix}} = \begin{bmatrix}x^{\prime} \\y^{\prime} \\z^{\prime} \\1\end{bmatrix}} & (1)\end{matrix}$

Where:

-   -   R_(ref) ^(T)—Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 1317 to the device reference frame 1319.    -   T_(ref)—Represents translation of the device reference frame        1317 to the device reference frame 1319.

One conventional approach to obtaining the Affine transform R (from axisunit vector u=(u_(x), u_(y), u_(z)), rotation angle θ) method.Wikipedia, at <http://en.wikipedia.org/wiki/Rotation_matrix>, Rotationmatrix from axis and angle, on Jan. 30, 2014, 20:12 UTC, upon which thecomputations equation (2) are at least in part inspired:

$\begin{matrix}{{R = \begin{bmatrix}{{\cos\mspace{14mu}\theta} + {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{x}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{u_{x}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} \\{{u_{y}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{y}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} \\{{u_{z}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} & {{u_{z}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix}}{R^{T} = {{\begin{bmatrix}{{\cos\mspace{14mu}\theta} + {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{y}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{u_{z}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} \\{{u_{x}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{u_{z}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} \\{{u_{x}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} & {{u_{y}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} & {{\cos\mspace{14mu}\theta} + {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix} - R^{T}} = \begin{bmatrix}{{{- \cos}\mspace{14mu}\theta} - {u_{x}^{2}( {1 - {\cos\;\theta}} )}} & {{{- u_{y}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{z}\sin\;\theta}} & {{{- u_{z}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} \\{{{- u_{x}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} & {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} & {{{- u_{z}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} \\{{{- u_{x}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} & {{{- u_{y}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} & {{{- \cos}\mspace{14mu}\theta} - {u_{z}^{2}( {1 - {\cos\;\theta}} )}}\end{bmatrix}}}{T = \begin{bmatrix}a \\b \\c\end{bmatrix}}} & (2)\end{matrix}$is a vector representing a translation of the object with respect toorigin of the coordinate system of the translated frame,

${{- R^{T}}*T} = \begin{bmatrix}{{( {{{- \cos}\mspace{14mu}\theta} - {u_{x}^{2}( {1 - {\cos\;\theta}} )}} )(a)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} )(b)} + {( {{{- u_{z}}{u_{x}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{y}\sin\;\theta}} )(c)}} \\{{( {{{- u_{x}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{z}\sin\;\theta}} )(a)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{y}^{2}( {1 - {\cos\;\theta}} )}} )(b)} + {( {{{- u_{z}}{u_{y}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{x}\sin\;\theta}} )(c)}} \\{{( {{{- u_{x}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} - {u_{y}\sin\;\theta}} )(a)} + {( {{{- u_{y}}{u_{z}( {1 - {\cos\mspace{14mu}\theta}} )}} + {u_{x}\sin\;\theta}} )(b)} + {( {{{- \cos}\mspace{14mu}\theta} - {u_{z}^{2}( {1 - {\cos\;\theta}} )}} )(c)}}\end{bmatrix}$

In another example, an orientation and position of the common featuresin three-dimensional map 1313 at time t=t₀: vector pair

$\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix},$can be converted to a orientation and position of the common features inthree-dimensional map 1315 at time t=t₁:

$\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}\quad$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}.$The correct orientation and position of the tracked common features withrespect to device reference frame at time t=t₀ (1317) is given by aninverse affine transformation, e.g.,

$\quad\begin{bmatrix}R_{ref}^{T} & {{- R_{ref}^{T}}*T_{ref}} \\0 & 1\end{bmatrix}$as provided for in equation (3):

$\begin{matrix}{{\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}*\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix}} = \begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}} & (3)\end{matrix}$

Where:

-   -   R^(T) _(ref)—Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 1317 to the device reference frame 1319.    -   R_(obj)—Represents a matrix describing the rotation at t₀ of the        common features with respect to the device reference frame 1319.    -   R′_(obj)—Represents a matrix describing the rotation at t₁ of        the common features with respect to the device reference frame        1317.    -   T_(ref)—Represents a vector translation of the device reference        frame 1317 to the device reference frame 1319.    -   T_(obj)—Represents a vector describing the position at t₀ of the        common features with respect to the device reference frame 1319.    -   T′_(obj)—Represents a vector describing the position at t₁ of        the common features with respect to the device reference frame        1317.

In a yet further example, an orientation and position of the commonfeatures in three-dimensional map 1313 at time t=t₀: affine transform

$\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix},$can be converted to an orientation and position of the common featuresin three-dimensional map 1315 at time t=t₁:

$\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}\quad$using an affine transform

$\begin{bmatrix}R_{ref} & T_{ref} \\0 & 1\end{bmatrix}.$Furthermore, the position and orientation of the initial reference framewith respect to a (typically) fixed reference point in space can bedetermined using an affine transform

$\begin{bmatrix}R_{init} & T_{init} \\0 & 1\end{bmatrix}.$The correct orientation and position of the tracked common features withrespect to device reference frame at time t=t₀ (1317) is given by aninverse affine transformation, e.g.,

$\begin{bmatrix}R_{init}^{T} & {( {- R_{init}^{T}} )*T_{init}} \\0 & 1\end{bmatrix}\quad$as provided for in equation (4):

$\begin{matrix}{{{\begin{bmatrix}R_{init}^{T} & {( {- R_{init}^{T}} )*T_{init}} \\0 & 1\end{bmatrix}\begin{bmatrix}R_{ref}^{T} & {( {- R_{ref}^{T}} )*T_{ref}} \\0 & 1\end{bmatrix}}\begin{bmatrix}R_{obj} & T_{obj} \\0 & 1\end{bmatrix}} = {\quad\begin{bmatrix}R_{obj}^{\prime} & T_{obj}^{\prime} \\0 & 1\end{bmatrix}}} & (4)\end{matrix}$

Where:

-   -   R^(T) _(init)—Represents a rotation matrix part of an affine        transform describing the rotation transformation at t₀ from the        world reference frame 1321 to the device reference frame 1317.    -   R^(T) _(ref)—Represents the rotation matrix part of an affine        transform describing the rotation transformation from the device        reference frame 1317 to the device reference frame 1319.    -   R_(obj)—Represents a matrix describing the rotation of the        common features at t₀ with respect to the device reference frame        1319.    -   R′_(obj)—Represents a matrix describing the rotation of the        common features at t₁ with respect to the device reference frame        1317.    -   T_(init)—Represents a vector translation at t₀ of the world        reference frame 1321 to the device reference frame 1317.    -   T_(ref)—Represents a vector translation at t₁ of the device        reference frame 1317 to the device reference frame 1319.    -   T_(obj)—Represents a vector describing the position at t₀ of the        common features with respect to the device reference frame 1319.    -   T′_(obj)—Represents a vector describing the position at t₁ of        the common features with respect to the device reference frame        1317.

In some implementations, the technology disclosed can build a worldmodel with an absolute or world frame of reference. The world model caninclude representations of object portions (e.g. objects, edges ofobjects, prominent vortices) and potentially depth information whenavailable from a depth sensor, depth camera or the like, within theviewpoint of the virtual or augmented reality HMD. The system can buildthe world model from image information captured by the cameras of thesensor. Points in 3D space can be determined from the stereo-imageinformation are analyzed to obtain object portions. These points are notlimited to a hand or other control object in a foreground; the points in3D space can include stationary background points, especially edges. Themodel is populated with the object portions.

Differences in three-dimensional maps are used to determine an inversetransformation

$( {{the}\mspace{14mu}\begin{bmatrix}R^{T} & {{- R^{T}}*T} \\0 & 1\end{bmatrix}} )$between model position and new position of common features in thethree-dimensional maps. In this affine transform, R^(T) describes therotational portions of motions between camera and object coordinatesystems, and T describes the translational portions thereof. The systemthen applies an inverse transformation of the three-dimensional mapscorresponding to the actual transformation of the three-dimensional mapsto determine the positional information of the HMDs, includingtranslation and rotation of the HMDs.

FIGS. 15, 16, 17, and 18 show one implementation of content sharingbetween wearable sensory systems like HMDs in a three-dimensionalsensory real world space 1500. In FIG. 15, users 1 and 2 are immersed invirtual and/or augmented environments using respective HMDs 1510 and1512. User 1's HMD 1512 includes multiple image capturing sources 1512and 1506 such that it captures first and second video streams of thereal word space 1500 at different fields of view. This configuration isonly exemplary and other implementations can include HMD 1512 having acamera with multiple lens that capture video streams of the real wordspace 1500 at different fields of view. In other implementations, camera1506 is embedded in the HMD 1512.

In one implementation, the field of view of camera 1506 substantiallyoverlaps with the field of view of HMD 1512 or vice-versa. In anotherimplementation, the field of view of camera 1506 encompasses and exceedsthe field of view of HMD 1512 or vice-versa. In yet anotherimplementation, the field of view of camera 1506 narrows and deceeds thefield of view of HMD 1512 or vice-versa.

In one implementation, the field of view of camera 1506 is separate andadditional to the field of view of HMD 1512 or vice-versa. In anotherimplementation, the field of view of camera 1506 is separate andadditional to the field of view of HMD 1512 or vice-versa. In yetanother implementation, the field of view of camera 1506 is separate andadditional to the field of view of HMD 1512 or vice-versa.

Some implementations include using short-beam illumination elements tocapture a narrow-field of view. Other implementations include theshort-beam illumination elements having a beam angle of approximately60°.

Some implementations include using wide-beam illumination elements tocapture a broad-field of view. Other implementations include thewide-beam illumination elements having a beam angle of approximately120°.

Referring again to FIG. 15, camera 1506's field of view 1504 is widerthan HMD 1512's field of view 1502, and therefore the resulting capturedframe 1608 from field of view 1504 in block 1604 of FIG. 16 encompassesmuch greater portion of the real world space 1500 than the capturedframe 1606 from field of view 1502 in block 1602 of FIG. 16, asillustrated in implementation 1600 of FIG. 16.

In addition, FIG. 17 depicts, in block 1702, captured frame 1706 fromfield of view 1508 of user 2's HMD 1510. HMD 1510's field of view 1508is much narrower than camera 1506's field of view 1504 and is alsoexactly opposing to field of view 1502 of HMD 1512. Implementation 1700shows captured frame 1706 includes a much smaller portion of real worldspace 1500 compared to captured frame 1608.

FIG. 18 shows one implementation 1800 of an augmented version of thecaptured images and video stream being transmitted to user 2 of HMD 1510in block 1802. The augmented version can include corresponding content,with the same captured frame 1706 as the original version, but capturedfrom a wider or more encompassing field of view 1504 than the originalversion. The augmented version can be further used to provide apanoramic experience to user 2 of the user 1's limited view 1502.

In one implementation, the captured content 1608 is pre-processed beforeit is transmitted to user 2. Pre-processing includes enhancing theresolution or contrast of the content or augmenting it with additionalgraphics, annotations, or comments, according to one implementation. Inother implementations, pre-processing includes reducing the resolutionof the captured content before transmission.

In some implementations, motion capture is achieved using an opticalmotion-capture system. In some implementations, object position trackingis supplemented by measuring a time difference of arrival (TDOA) ofaudio signals at the contact vibrational sensors and mapping surfacelocations that satisfy the TDOA, analyzing at least one image, capturedby a camera of the optical motion-capture system, of the object incontact with the surface, and using the image analysis to select amongthe mapped TDOA surface locations as a surface location of the contact.

Reference may be had to the following sources, incorporated herein byreference, for further information regarding computational techniques:

-   1. Wikipedia, at <http://en.wikipedia.org/wiki/Euclidean_group>, on    Nov. 4, 2013, 04:08 UTC;-   2. Wikipedia, at    <http://en.wikipedia.org/wiki/Affine_transformation>, on Nov. 25,    2013, 11:01 UTC;-   3. Wikipedia, at <http://en.wikipedia.org/wiki/Rotation_matrix>,    Rotation matrix from axis and angle, on Jan. 30, 2014, 20:12 UTC;-   4. Wikipedia, at    <http://en.wikipedia.org/wiki/Rotation_group_SO(3)>, Axis of    rotation, on Jan. 21, 2014, 21:21 UTC;-   5. Wikipedia, at    <http://en.wikipedia.org/wiki/Transformation_matrix>, Affine    Transformations, on Jan. 28, 2014, 13:51 UTC;-   6. Wikipedia, at <http://en.wikipedia.org/wiki/Axis %    E2%80%93angle_representation>, on Jan. 25, 2014, 03:26 UTC;-   7. Wikipedia, at <http://en.wikipedia.org/wiki/Visual_odometry>, on    Jun. 26, 2014, 09:38 UTC; and-   8. Wikipedia, at <http://en.wikipedia.org/wiki/Optical_flow>, on    Jun. 26, 2014, 09:38 UTC.

While the disclosed technology has been described with respect tospecific implementations, one skilled in the art will recognize thatnumerous modifications are possible. The number, types and arrangementof cameras and sensors can be varied. The cameras' capabilities,including frame rate, spatial resolution, and intensity resolution, canalso be varied as desired. The sensors' capabilities, includingsensitively levels and calibration, can also be varied as desired. Lightsources are optional and can be operated in continuous or pulsed mode.The systems described herein provide images and audio signals tofacilitate tracking movement of an object, and this information can beused for numerous purposes, of which position and/or motion detection isjust one among many possibilities.

Threshold cutoffs and other specific criteria for distinguishing objectfrom background can be adapted for particular hardware and particularenvironments. Frequency filters and other specific criteria fordistinguishing visual or audio signals from background noise can beadapted for particular cameras or sensors and particular devices. Insome implementations, the system can be calibrated for a particularenvironment or application, e.g., by adjusting frequency filters,threshold criteria, and so on.

Any type of object can be the subject of motion capture using thesetechniques, and various aspects of the implementation can be optimizedfor a particular object. For example, the type and positions of camerasand/or other sensors can be selected based on the size of the objectwhose motion is to be captured, the space in which motion is to becaptured, and/or the medium of the surface through which audio signalspropagate. Analysis techniques in accordance with implementations of thetechnology disclosed can be implemented as algorithms in any suitablecomputer language and executed on programmable processors.Alternatively, some or all of the algorithms can be implemented infixed-function logic circuits, and such circuits can be designed andfabricated using conventional or other tools.

Computer programs incorporating various features of the technologydisclosed may be encoded on various computer readable storage media;suitable media include magnetic disk or tape, optical storage media suchas compact disk (CD) or DVD (digital versatile disk), flash memory, andany other non-transitory medium capable of holding data in acomputer-readable form. Computer-readable storage media encoded with theprogram code may be packaged with a compatible device or providedseparately from other devices. In addition program code may be encodedand transmitted via wired optical, and/or wireless networks conformingto a variety of protocols, including the Internet, thereby allowingdistribution, e.g., via Internet download.

Particular Implementations

The methods described in this section and other sections of thetechnology disclosed can include one or more of the following featuresand/or features described in connection with additional methodsdisclosed. In the interest of conciseness, the combinations of featuresdisclosed in this application are not individually enumerated and arenot repeated with each base set of features. The reader will understandhow features identified in this section can readily be combined withsets of base features identified as implementations such as pervasivecomputing environment, hand-held mode, wide-area mode, augmentedreality, embedding architectures, rigged hand, biometrics, etc.

These methods can be implemented at least partially with a databasesystem, e.g., by one or more processors configured to receive orretrieve information, process the information, store results, andtransmit the results. Other implementations may perform the actions indifferent orders and/or with different, fewer or additional actions thanthose discussed. Multiple actions can be combined in someimplementations. For convenience, these methods is described withreference to the system that carries out a method. The system is notnecessarily part of the method.

Other implementations of the methods described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the methods described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Some example implementations are listed below with certainimplementations dependent upon the implementation to which they referto:

1. A method of tracking motion of a wearable sensor system, the methodincluding:

capturing a video stream of a scene of a real world space using at leastone camera electronically coupled to a wearable sensor system;

using a set of RGB pixels and a set of IR pixels of the camera,detecting one or more feature values of the scene from a plurality ofimages of the video stream captured at times t0 and t1, wherein thewearable sensor system moved between t0 and t1; and

-   -   determining motion information of the wearable sensor system        with respect to at least one feature of the scene based on        comparison between feature values detected at times t0 and t1.        2. The method of implementation 1, further including generating        for display, across an interface of the wearable sensor system,        a presentation output based on information from the sets of RGB        and IR pixels.        3. The method of implementation 2, further including        automatically calibrating responsiveness of the presentation        output based on the determined motion information of the        wearable sensor system with respect to the at least one feature        of the scene.        4. The method of implementation 3, further including        proportionally adjusting perceived field of view of the        presentation output responsive to the determined motion        information of the wearable sensor system with respect to the at        least one feature of the scene.        5. The method of implementation 1, further including determining        motion information of a body portion engaged with the wearable        sensory system based on the motion information of the wearable        sensor system.        6. The method of implementation 1, further including extracting        gross features of the real world space using RGB pixels that        respectively capture red, green, and blue components of        illumination in the scene.        7. The method of implementation 1, further including extracting        fine features of the real world space using IR pixels that        capture infrared components of illumination in the scene.        8. The method of implementation 7, wherein fine features of the        real world space include surface texture of the real world        space.        9. The method of implementation 7, wherein fine features of the        real world space include edges of the real world space.        10. The method of implementation 7, wherein fine features of the        real world space include curvatures of the real world space.        11. The method of implementation 7, wherein fine features of the        real world space include surface texture of objects in the real        world space.        12. The method of implementation 7, wherein fine features of the        real world space include edges of objects in the real world        space.        13. The method of implementation 7, wherein fine features of the        real world space include curvatures of objects in the real world        space.        14. The method of implementation 1, wherein a feature of the        scene is an object in the real world space.        15. The method of implementation 14, wherein a feature value of        the scene is orientation of the object.        16. The method of implementation 14, wherein a feature value of        the scene is position of the object.        17. The method of implementation 1, wherein a feature of the        scene is an arrangement of plurality of objects in the real        world space.        18. The method of implementation 17, wherein a feature value of        the scene is position of the objects with respect to each other        in the arrangement.        19. The method of implementation 1, wherein comparison between        feature values includes detecting a change in rotation between        the images captured at times t0 and t1.        20. The method of implementation 1, wherein comparison between        feature values includes detecting a change in translation        between the images captured at times t0 and t1.        21. The method of implementation 1, further including        determining motion information of the wearable sensor system        with respect to at least one feature of the scene by matching        features in images captured at time t0 with corresponding        features in images captured at time t1, wherein the matched        features are within a threshold distance.        22. The method of implementation 1, further including        determining motion information of the wearable sensor system        with respect to at least one feature of the scene by calculating        displacement between the images captured at times t0 and t1        based on at least one of RGB and IR pixel values.        23. The method of implementation 1, wherein the motion        information includes position of the wearable sensor system.        24. The method of implementation 1, wherein the motion        information includes orientation of the wearable sensor system.        25. The method of implementation 1, wherein the motion        information includes velocity of the wearable sensor system.        26. The method of implementation 1, wherein the motion        information includes acceleration of the wearable sensor system.        27. The method of implementation 1, further including using        monocular vision to capture the video stream.        28. The method of implementation 1, further including using        stereoscopic vision to capture the video stream.        29. The method of implementation 1, the images captured at times        t0 and t1 are successive image pairs.        30. The method of implementation 1, the images captured at times        t0 and t1 are alternative image pairs.        31. The method of implementation 1, the images captured at times        t0 and t1 are alternative image pairs.        32. The method of implementation 1, the images captured are        right and left stereo images captured simultaneously.        33. A method of creating a multi-user interactive virtual        environment using wearable sensor systems, the method including:

capturing a first video stream of a real world space using at least onecamera electronically coupled to a first wearable sensor system engagedby a first user;

capturing a second video stream of a real world space using at least onecamera electronically coupled to a second wearable sensor system engagedby a second user;

using sets of RGB and IR pixels of the first and second cameras,generating respective three-dimensional maps of the real world space byextracting one or more feature values of the real world space from thefirst and second video streams; and

determining motion information of the first and second wearable sensorsystems with respect to each other based on comparison between therespective three-dimensional maps of the real world space.

34. The method of implementation 33, wherein generating respectivethree-dimensional maps further includes determining a graph of featuresof the real world space based on the extracted feature values.

35. The method of implementation 33, further including generating fordisplay, across respective interfaces of the first and second wearablesensor systems, presentation outputs based on information from the setsof RGB and IR pixels of the first and second cameras.36. The method of implementation 35, further including automaticallycalibrating responsiveness of the presentation outputs based on thedetermined motion information of the first and second wearable sensorsystems with respect to each other.37. The method of implementation 36, further including proportionallyadjusting respective perceived fields of view of the presentationoutputs responsive to the determined motion information of the first andsecond wearable sensor systems with respect to each other.38. The method of implementation 33, further including determiningmotion information of respective body portions of the first and secondusers based on the motion information of the first and second wearablesensor systems with respect to each other.39. The method of implementation 33, further including extracting grossfeatures of the real world space using RGB pixels that respectivelycapture red, green, and blue components of illumination in the realworld space.40. The method of implementation 33, further including extracting finefeatures of the real world space using IR pixels that capture infraredcomponents of illumination in the real world space.41. The method of implementation 40, wherein fine features of the realworld space include surface texture of the real world space.42. The method of implementation 40, wherein fine features of the realworld space include edges of the real world space.43. The method of implementation 40, wherein fine features of the realworld space include curvatures of the real world space.44. The method of implementation 40, wherein fine features of the realworld space include surface texture of objects in the real world space.45. The method of implementation 40, wherein fine features of the realworld space include edges of objects in the real world space.46. The method of implementation 40, wherein fine features of the realworld space include curvatures of objects in the real world space.47. The method of implementation 33, wherein a feature of the real worldspace is an object in the real world space.48. The method of implementation 47, wherein a feature value of the realworld space is orientation of the object.49. The method of implementation 47, wherein a feature value of the realworld space is position of the object.50. The method of implementation 33, wherein a feature of the real worldspace is an arrangement of plurality of objects in the real world space.51. The method of implementation 50, wherein a feature value of the realworld space is position of the objects with respect to each other in thearrangement.52. The method of implementation 33, wherein comparison between featurevalues includes detecting a change in rotation between the imagescaptured at times t0 and t1.53. The method of implementation 33, wherein comparison between featurevalues includes detecting a change in translation between the imagescaptured at times t0 and t1.54. The method of implementation 33, further including determiningmotion information of the wearable sensor system with respect to atleast one feature of the real world space by matching features in imagescaptured at time t0 with corresponding features in images captured attime t1, wherein the matched features are within a threshold distance.55. The method of implementation 33, further including determiningmotion information of the wearable sensor system with respect to atleast one feature of the real world space by calculating displacementbetween the images captured at times t0 and t1 based on at least one ofRGB and IR pixel values.56. The method of implementation 33, wherein the motion information ofthe first and second wearable sensor systems includes respectivepositions of the wearable sensor system.57. The method of implementation 33, wherein the motion information ofthe first and second wearable sensor systems includes respectiveorientations of the wearable sensor system.58. The method of implementation 33, wherein the motion information ofthe first and second wearable sensor systems includes respectivevelocities of the wearable sensor system.59. The method of implementation 33, wherein the motion information ofthe first and second wearable sensor systems includes respectiveaccelerations of the wearable sensor system.60. A method of sharing content between wearable sensor systems, themethod including:

capturing a first video stream of a real world space at time t0 using atleast one camera electronically coupled to a first wearable sensorsystem engaged by a first user, wherein the first video stream iscaptured at a field of view of the first user;

capturing a second video stream of the real world space at the time t0using at least one camera electronically coupled to the first wearablesensor system, wherein the second video stream is captured at a field ofview of the camera; and

establishing a communication channel between the first wearable sensorsystem and a second wearable sensor system and transmitting the secondvideo stream to the second wearable sensor system.

61. The method of implementation 60, further including preprocessing thesecond video stream to enhance resolution and sending the preprocessedsecond video stream via the communication channel to the second wearablesensor system.

62. The method of implementation 60, further including preprocessing thesecond video stream to reduce bandwidth and sending the preprocessedsecond video stream via the communication channel to the second wearablesensor system.

63. The method of implementation 60, wherein the field of view of the atleast one camera substantially overlaps with the field of view of theuser.

64. The method of implementation 60, wherein the field of view of the atleast one camera encompasses and exceeds the field of view of the user.

65. The method of implementation 60, wherein the field of view of the atleast one camera narrows and deceeds the field of view of the user.

66. The method of implementation 60, wherein the field of view of the atleast one camera is separate and additional to the field of view of theuser.

67. The method of implementation 60, further including using short-beamillumination elements to capture a narrow-field of view.

68. The method of implementation 67, wherein the short-beam illuminationelements have a beam angle of approximately 60°.

69. The method of implementation 60, further including using wide-beamillumination elements to capture a broad-field of view.

70. The method of implementation 69, wherein the wide-beam illuminationelements have a beam angle of approximately 120°.

71. The method of implementation 60, further including transmitting thesecond video stream to the second sensor system in response to userselection.

The terms and expressions employed herein are used as terms andexpressions of description and not of limitation, and there is nointention, in the use of such terms and expressions, of excluding anyequivalents of the features shown and described or portions thereof. Inaddition, having described certain implementations of the technologydisclosed, it will be apparent to those of ordinary skill in the artthat other implementations incorporating the concepts disclosed hereincan be used without departing from the spirit and scope of thetechnology disclosed. Accordingly, the described implementations are tobe considered in all respects as only illustrative and not restrictive.

What is claimed is:
 1. A system for creating a multi-user interactivevirtual environment, the system including: a plurality of wearablesensor systems for displaying a virtual reality to a user wearing thesystem, including first wearable sensor system engaged by a first userand a second wearable sensor system engaged by a second user; aplurality of cameras, wherein at least one camera is electronicallycoupled to each of a wearable sensor system of the plurality of wearablesensor systems; and one or more processors coupled to a memory storinginstructions for creating a multi-user interactive virtual environment,which instructions when executed by one or more processors perform:capturing a first video stream of a real world space using at least onecamera electronically coupled to the first wearable sensor systemengaged by a first user; capturing a second video stream of a real worldspace using at least one camera electronically coupled to the secondwearable sensor system engaged by a second user; using images from thefirst and second video streams captured by the camera coupled to thefirst wearable sensor system and by the camera coupled to the secondwearable sensor system, generating respective three-dimensional maps ofa real world space by extracting one or more feature values from thereal world space from the first and second video streams; determiningmotion information of the first and second wearable sensor systems withrespect to each other based on comparison between the respectivethree-dimensional maps of the real world space; extracting body portionmovement information captured by the first and second wearable sensorsystem with respect to a moving reference frame, including: repeatedlydetermining movement information for the wearable sensor system and atleast one body portion at successive times to form a sequence ofmovement information; and analyzing the sequence of movement informationformed to determine a path of the body portion; tracking movement of thebody portion along the path of the body portion over a region of thereal world space; comparing the path tracked to a plurality of pathtemplates and identifying a template that best matches the path; andusing the template that matches the path to obtain control informationto control an external system.
 2. The system of claim 1, wherein theinstructions for generating respective three-dimensional maps furtherincludes instructions that when executed perform determining a graph offeatures of the real world space based on the one or more feature valuesextracted.
 3. The system of claim 1, further including instructions thatwhen executed perform generating for display, across respectiveinterfaces of the first and second wearable sensor systems, presentationoutputs based on information from the images from the first and secondvideo streams captured by the camera coupled to the first wearablesensor system and the camera coupled to the second wearable sensorsystem.
 4. The system of claim 3, further including instructions thatwhen executed perform automatically calibrating responsiveness of thepresentation outputs based on the motion information of the first andsecond wearable sensor systems with respect to each other.
 5. The systemof claim 4, further including instructions that when executed performproportionally adjusting respective perceived fields of view of thepresentation outputs responsive to the motion information of the firstand second wearable sensor systems with respect to each other.
 6. Thesystem of claim 1, further including instructions that when executedperform sharing content between wearable sensor systems; wherein thefirst video stream of a real world space is captured at a time t0 usingat least one camera electronically coupled to a first wearable sensorsystem engaged by a first user, wherein the first video stream iscaptured at a field of view of the first user; wherein the second videostream of the real world space is captured at the time t0 using at leastone camera electronically coupled to the first wearable sensor system,wherein the second video stream is captured at a field of view of thecamera; and establishing a communication channel between the firstwearable sensor system and a second wearable sensor system andtransmitting the second video stream to the second wearable sensorsystem.
 7. The system of claim 6, wherein the field of view of the atleast one camera substantially overlaps with the field of view of a userof the at least one camera.
 8. The system of claim 6, wherein the fieldof view of the at least one camera encompasses and exceeds the field ofview of a user of the at least one camera.
 9. The system of claim 6,wherein the field of view of the at least one camera narrows and deceedsthe field of view of a user of the at least one camera.
 10. The systemof claim 6, wherein the field of view of the at least one camera isseparate and additional to the field of view of a user of the at leastone camera.
 11. The system of claim 6, further including usingshort-beam illumination elements to capture a narrow-field of view. 12.A method for creating a multi-user interactive virtual environment, themethod including: capturing a first video stream of a real world spaceusing at least one camera electronically coupled to the first wearablesensor system engaged by a first user; capturing a second video streamof a real world space using at least one camera electronically coupledto the second wearable sensor system engaged by a second user; usingimages from the first and second video streams captured by the cameracoupled to the first wearable sensor system and by the camera coupled tothe second wearable sensor system, generating respectivethree-dimensional maps of a real world space by extracting one or morefeature values from the real world space from the first and second videostreams; determining motion information of the first and second wearablesensor systems with respect to each other based on comparison betweenthe respective three-dimensional maps of the real world space;extracting body portion movement information captured by the first andsecond wearable sensor system with respect to a moving reference frame,including: repeatedly determining movement information for the wearablesensor system and at least one body portion at successive times to forma sequence of movement information; and analyzing the sequence ofmovement information formed to determine a path of the body portion;tracking movement of the body portion along the path of the body portionover a region of the real world space; comparing the path tracked to aplurality of path templates and identifying a template that best matchesthe path; and using the template that matches the path to obtain controlinformation to control an external system.
 13. The method of claim 12,further including: sharing content between wearable sensor systems;wherein the first video stream of a real world space is captured at atime t0 using at least one camera electronically coupled to a firstwearable sensor system engaged by a first user, wherein the first videostream is captured at a field of view of the first user; wherein thesecond video stream of the real world space is captured at the time t0using at least one camera electronically coupled to the first wearablesensor system, wherein the second video stream is captured at a field ofview of the camera; and establishing a communication channel between thefirst wearable sensor system and a second wearable sensor system andtransmitting the second video stream to the second wearable sensorsystem.
 14. A non-transitory computer readable medium storinginstructions for creating a multi-user interactive virtual environment,which instructions when executed by one or more processors perform:capturing a first video stream of a real world space using at least onecamera electronically coupled to the first wearable sensor systemengaged by a first user; capturing a second video stream of a real worldspace using at least one camera electronically coupled to the secondwearable sensor system engaged by a second user; using images from thefirst and second video streams captured by the camera coupled to thefirst wearable sensor system and by the camera coupled to the secondwearable sensor system, generating respective three-dimensional maps ofa real world space by extracting one or more feature values from thereal world space from the first and second video streams; determiningmotion information of the first and second wearable sensor systems withrespect to each other based on comparison between the respectivethree-dimensional maps of the real world space; extracting body portionmovement information captured by the first and second wearable sensorsystem with respect to a moving reference frame, including: repeatedlydetermining movement information for the wearable sensor system and atleast one body portion at successive times to form a sequence ofmovement information; and analyzing the sequence of movement informationformed to determine a path of the body portion; tracking movement of thebody portion along the path of the body portion over a region of thereal world space; comparing the path tracked to a plurality of pathtemplates and identifying a template that best matches the path; andusing the template that matches the path to obtain control informationto control an external system.
 15. The non-transitory computer readablemedium of claim 14, further including instructions for: sharing contentbetween wearable sensor systems; wherein the first video stream of areal world space is captured at a time t0 using at least one cameraelectronically coupled to a first wearable sensor system engaged by afirst user, wherein the first video stream is captured at a field ofview of the first user; wherein the second video stream of the realworld space is captured at the time t0 using at least one cameraelectronically coupled to the first wearable sensor system, wherein thesecond video stream is captured at a field of view of the camera; andestablishing a communication channel between the first wearable sensorsystem and a second wearable sensor system and transmitting the secondvideo stream to the second wearable sensor system.